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EEG Signal Acquisition De-Noising and Classification for Brain Computer Interfaces
Pavan RamkumarGirish Singhal
Department of Electronics and Communication EngineeringIndian Institute of Technology
Guwahati
Supervisor Dr S Dandapat
Neurological signals as a Biometricbull Biological systems offer potential features for uniquely characterizing individualsbull High entropy and uniqueness of the neurological activity are sparsely explored for biometric authentication
Why Electroencephalogram (EEG) Signalbull In contrast to global anatomical information obtained from expensive imaging (fMRI) EEG offers highdensity functional information specific to the mental taskbull Acquisition system is relatively inexpensive to build
Project Overview
Motivationbull Physiological studies have tried to investigate correlation between EEG and genetic information [Vogel 1970]bull more secure non-manipulativebull Identity can be established in real time (unlike bio chemical tests DNA etc)
Goalbull Real-time implementation of biometric authentication for a closed set of users
Project Overviewhellip
Prior Art using EEGbull M Poulos et al Task eyes closed Features AR model FFT Bilinear model Classifier LVQbull R Palaniappan et al Task Viewing of standardized images [Vanderwart 1980] relevant to memory and cognitionbull Features PSD Classifier ANN
Our workbull Task Data fusion tasks are highly cognitive We have experimented with stereopsis (binocular fusion fordepth perception)bull Features Linear Prediction Modelbull Classifier Two stage classification using K-NN and SVM
Project Overviewhellip
Modular Flow
Motivation Development of a real time system
Issues Amplification Digitization De-noising
Motivation De-noising from contributing bio-signals improving soundness of features extracted
Issues Choice of optimization algorithm and objective function for ICA
Motivation Selecting of appropriate task features and classifying machine
Issues Comparative study of perceptive (stereopsis) and non-perceptive (mental arithmetic) tasks scope of rejection in classifier
Module I Acquisition SystemObjective To develop a real time EEG acquisition system
Impedance Matching Clamping Diodes for user safety Protection Circuit
SolutionDesign Issue
Sallen Key Low Pass filter (70Hz 40dBdecade)
Digitization using PCL-HG818 card
(200Hz pacer trigger 5Vp-p)
Digitization and Anti-aliasing
Tunable Q Twin Tee Notch with feedbackLine noise suppression
Two stage Sallen Key High Pass filter
(01Hz -80dBdecade)
DC Drift accumulation at electrodes
Prevention of saturation
Two stage Amplifier total gain 10000 Stage 1 IA (AD521) Gain 25 CMRR 100dB Stage 2 HG non-inverting amplifier (~ 500)
Differential Gain
9V
9V
11
13
10
14
2
1
3
127
To DAC
5k
5k
Rg
Rs
R1 R2
R3
C3
C1 C2
100k
C1 C2
R1
R2
R3 R4
R1
C1 C2R2
R3 R4
R3R4
R1
C1
C2
R2
Instrumentation Amplifier AD521
Key Formulae Vout = Ad Vd + Acm Vcm
Component values Rs = 100kRg = 10k
Circuit parameters Gain RsRg = 10CMRR 10
Twin T High Q Notch Filter
Key Formulae Fcutoff = 1(2piRC)
Component Values R1 = R2 = 2R3 = R = 3183 kC1 = C2 = C32 = C = 1uF
Circuit Parameters Gain = 1Fcutoff = 50 HzRoll of =
2 Stage Sallen Key High pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 68kC1 = C2 = C = 22uF
Circuit Parameters Gain = 15861586 for maximally flat responseFcutoff = 1 HzRoll of = -80dBdecade
Single Stage Sallen Key Low pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 137kC1 = C2 = C = 01uF
Circuit Parameters Gain = 1586 for maximally flat responseFcutoff = 70 HzRoll of = -40dBdecade
Single Stage Amplification
Component Values R1 = 100k POTR2 = 100 ohms
Circuit Parameters Gain = 0 to 1000
AD521741
741
741 741
741741
Instrumentation Amplifier Twin T High Q Notch Filter 2 Stage Sallen Key High Pass Filter
Sallen Key Low Pass FilterAmplification stage
EEG Acquisition System
Driven Right Leg Circuit High CMRR Shielded probes
FET based instrumentation amplifiers Differential amplifiers have high input impedance Negligible input bias current Low amplifier noise
De-noising using software Discrete Filtering Independent Component Analysis (ICA)
Future Work
Objective To design experimental tasks that maximize uniqueness of features extracted
What are the desired characteristics
bull Must be easy to perform for user
bull Designing universal protocol for time invariability1048766 standardized lighting conditions1048766 gaze localization1048766 texture invariance1048766 body posture constancy
Module II Design of Experiments
StereopsisWhat is Stereopsis
bull Computation of visual depth from retinal binocular disparitybull Signals from each retina reach the visual cortex via independent pathways
Why Stereopsis
bull Highly cognitive task involving multi-sensor integration
bull Binocular fusion shows a higher FFT activity as observed from electrode P4 in MEG recordings
bull Pre-cognitive perceptive task and hence resulting EEG patterns are relatively immune to voluntary distortion by un co-operative users
Hypothesis May provide better features
Stereopsishellip
Courtesy U Shahani et al Neuroscience Letters 315 (2001) 154ndash158
StereopsishellipMEG Results from Literature
Stereopsishellipbull Verification of MEG results bull EEG recordings at IIT Guwahatibull Stereograms are used to eliminate monocular cues from depth
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Neurological signals as a Biometricbull Biological systems offer potential features for uniquely characterizing individualsbull High entropy and uniqueness of the neurological activity are sparsely explored for biometric authentication
Why Electroencephalogram (EEG) Signalbull In contrast to global anatomical information obtained from expensive imaging (fMRI) EEG offers highdensity functional information specific to the mental taskbull Acquisition system is relatively inexpensive to build
Project Overview
Motivationbull Physiological studies have tried to investigate correlation between EEG and genetic information [Vogel 1970]bull more secure non-manipulativebull Identity can be established in real time (unlike bio chemical tests DNA etc)
Goalbull Real-time implementation of biometric authentication for a closed set of users
Project Overviewhellip
Prior Art using EEGbull M Poulos et al Task eyes closed Features AR model FFT Bilinear model Classifier LVQbull R Palaniappan et al Task Viewing of standardized images [Vanderwart 1980] relevant to memory and cognitionbull Features PSD Classifier ANN
Our workbull Task Data fusion tasks are highly cognitive We have experimented with stereopsis (binocular fusion fordepth perception)bull Features Linear Prediction Modelbull Classifier Two stage classification using K-NN and SVM
Project Overviewhellip
Modular Flow
Motivation Development of a real time system
Issues Amplification Digitization De-noising
Motivation De-noising from contributing bio-signals improving soundness of features extracted
Issues Choice of optimization algorithm and objective function for ICA
Motivation Selecting of appropriate task features and classifying machine
Issues Comparative study of perceptive (stereopsis) and non-perceptive (mental arithmetic) tasks scope of rejection in classifier
Module I Acquisition SystemObjective To develop a real time EEG acquisition system
Impedance Matching Clamping Diodes for user safety Protection Circuit
SolutionDesign Issue
Sallen Key Low Pass filter (70Hz 40dBdecade)
Digitization using PCL-HG818 card
(200Hz pacer trigger 5Vp-p)
Digitization and Anti-aliasing
Tunable Q Twin Tee Notch with feedbackLine noise suppression
Two stage Sallen Key High Pass filter
(01Hz -80dBdecade)
DC Drift accumulation at electrodes
Prevention of saturation
Two stage Amplifier total gain 10000 Stage 1 IA (AD521) Gain 25 CMRR 100dB Stage 2 HG non-inverting amplifier (~ 500)
Differential Gain
9V
9V
11
13
10
14
2
1
3
127
To DAC
5k
5k
Rg
Rs
R1 R2
R3
C3
C1 C2
100k
C1 C2
R1
R2
R3 R4
R1
C1 C2R2
R3 R4
R3R4
R1
C1
C2
R2
Instrumentation Amplifier AD521
Key Formulae Vout = Ad Vd + Acm Vcm
Component values Rs = 100kRg = 10k
Circuit parameters Gain RsRg = 10CMRR 10
Twin T High Q Notch Filter
Key Formulae Fcutoff = 1(2piRC)
Component Values R1 = R2 = 2R3 = R = 3183 kC1 = C2 = C32 = C = 1uF
Circuit Parameters Gain = 1Fcutoff = 50 HzRoll of =
2 Stage Sallen Key High pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 68kC1 = C2 = C = 22uF
Circuit Parameters Gain = 15861586 for maximally flat responseFcutoff = 1 HzRoll of = -80dBdecade
Single Stage Sallen Key Low pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 137kC1 = C2 = C = 01uF
Circuit Parameters Gain = 1586 for maximally flat responseFcutoff = 70 HzRoll of = -40dBdecade
Single Stage Amplification
Component Values R1 = 100k POTR2 = 100 ohms
Circuit Parameters Gain = 0 to 1000
AD521741
741
741 741
741741
Instrumentation Amplifier Twin T High Q Notch Filter 2 Stage Sallen Key High Pass Filter
Sallen Key Low Pass FilterAmplification stage
EEG Acquisition System
Driven Right Leg Circuit High CMRR Shielded probes
FET based instrumentation amplifiers Differential amplifiers have high input impedance Negligible input bias current Low amplifier noise
De-noising using software Discrete Filtering Independent Component Analysis (ICA)
Future Work
Objective To design experimental tasks that maximize uniqueness of features extracted
What are the desired characteristics
bull Must be easy to perform for user
bull Designing universal protocol for time invariability1048766 standardized lighting conditions1048766 gaze localization1048766 texture invariance1048766 body posture constancy
Module II Design of Experiments
StereopsisWhat is Stereopsis
bull Computation of visual depth from retinal binocular disparitybull Signals from each retina reach the visual cortex via independent pathways
Why Stereopsis
bull Highly cognitive task involving multi-sensor integration
bull Binocular fusion shows a higher FFT activity as observed from electrode P4 in MEG recordings
bull Pre-cognitive perceptive task and hence resulting EEG patterns are relatively immune to voluntary distortion by un co-operative users
Hypothesis May provide better features
Stereopsishellip
Courtesy U Shahani et al Neuroscience Letters 315 (2001) 154ndash158
StereopsishellipMEG Results from Literature
Stereopsishellipbull Verification of MEG results bull EEG recordings at IIT Guwahatibull Stereograms are used to eliminate monocular cues from depth
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Motivationbull Physiological studies have tried to investigate correlation between EEG and genetic information [Vogel 1970]bull more secure non-manipulativebull Identity can be established in real time (unlike bio chemical tests DNA etc)
Goalbull Real-time implementation of biometric authentication for a closed set of users
Project Overviewhellip
Prior Art using EEGbull M Poulos et al Task eyes closed Features AR model FFT Bilinear model Classifier LVQbull R Palaniappan et al Task Viewing of standardized images [Vanderwart 1980] relevant to memory and cognitionbull Features PSD Classifier ANN
Our workbull Task Data fusion tasks are highly cognitive We have experimented with stereopsis (binocular fusion fordepth perception)bull Features Linear Prediction Modelbull Classifier Two stage classification using K-NN and SVM
Project Overviewhellip
Modular Flow
Motivation Development of a real time system
Issues Amplification Digitization De-noising
Motivation De-noising from contributing bio-signals improving soundness of features extracted
Issues Choice of optimization algorithm and objective function for ICA
Motivation Selecting of appropriate task features and classifying machine
Issues Comparative study of perceptive (stereopsis) and non-perceptive (mental arithmetic) tasks scope of rejection in classifier
Module I Acquisition SystemObjective To develop a real time EEG acquisition system
Impedance Matching Clamping Diodes for user safety Protection Circuit
SolutionDesign Issue
Sallen Key Low Pass filter (70Hz 40dBdecade)
Digitization using PCL-HG818 card
(200Hz pacer trigger 5Vp-p)
Digitization and Anti-aliasing
Tunable Q Twin Tee Notch with feedbackLine noise suppression
Two stage Sallen Key High Pass filter
(01Hz -80dBdecade)
DC Drift accumulation at electrodes
Prevention of saturation
Two stage Amplifier total gain 10000 Stage 1 IA (AD521) Gain 25 CMRR 100dB Stage 2 HG non-inverting amplifier (~ 500)
Differential Gain
9V
9V
11
13
10
14
2
1
3
127
To DAC
5k
5k
Rg
Rs
R1 R2
R3
C3
C1 C2
100k
C1 C2
R1
R2
R3 R4
R1
C1 C2R2
R3 R4
R3R4
R1
C1
C2
R2
Instrumentation Amplifier AD521
Key Formulae Vout = Ad Vd + Acm Vcm
Component values Rs = 100kRg = 10k
Circuit parameters Gain RsRg = 10CMRR 10
Twin T High Q Notch Filter
Key Formulae Fcutoff = 1(2piRC)
Component Values R1 = R2 = 2R3 = R = 3183 kC1 = C2 = C32 = C = 1uF
Circuit Parameters Gain = 1Fcutoff = 50 HzRoll of =
2 Stage Sallen Key High pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 68kC1 = C2 = C = 22uF
Circuit Parameters Gain = 15861586 for maximally flat responseFcutoff = 1 HzRoll of = -80dBdecade
Single Stage Sallen Key Low pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 137kC1 = C2 = C = 01uF
Circuit Parameters Gain = 1586 for maximally flat responseFcutoff = 70 HzRoll of = -40dBdecade
Single Stage Amplification
Component Values R1 = 100k POTR2 = 100 ohms
Circuit Parameters Gain = 0 to 1000
AD521741
741
741 741
741741
Instrumentation Amplifier Twin T High Q Notch Filter 2 Stage Sallen Key High Pass Filter
Sallen Key Low Pass FilterAmplification stage
EEG Acquisition System
Driven Right Leg Circuit High CMRR Shielded probes
FET based instrumentation amplifiers Differential amplifiers have high input impedance Negligible input bias current Low amplifier noise
De-noising using software Discrete Filtering Independent Component Analysis (ICA)
Future Work
Objective To design experimental tasks that maximize uniqueness of features extracted
What are the desired characteristics
bull Must be easy to perform for user
bull Designing universal protocol for time invariability1048766 standardized lighting conditions1048766 gaze localization1048766 texture invariance1048766 body posture constancy
Module II Design of Experiments
StereopsisWhat is Stereopsis
bull Computation of visual depth from retinal binocular disparitybull Signals from each retina reach the visual cortex via independent pathways
Why Stereopsis
bull Highly cognitive task involving multi-sensor integration
bull Binocular fusion shows a higher FFT activity as observed from electrode P4 in MEG recordings
bull Pre-cognitive perceptive task and hence resulting EEG patterns are relatively immune to voluntary distortion by un co-operative users
Hypothesis May provide better features
Stereopsishellip
Courtesy U Shahani et al Neuroscience Letters 315 (2001) 154ndash158
StereopsishellipMEG Results from Literature
Stereopsishellipbull Verification of MEG results bull EEG recordings at IIT Guwahatibull Stereograms are used to eliminate monocular cues from depth
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Prior Art using EEGbull M Poulos et al Task eyes closed Features AR model FFT Bilinear model Classifier LVQbull R Palaniappan et al Task Viewing of standardized images [Vanderwart 1980] relevant to memory and cognitionbull Features PSD Classifier ANN
Our workbull Task Data fusion tasks are highly cognitive We have experimented with stereopsis (binocular fusion fordepth perception)bull Features Linear Prediction Modelbull Classifier Two stage classification using K-NN and SVM
Project Overviewhellip
Modular Flow
Motivation Development of a real time system
Issues Amplification Digitization De-noising
Motivation De-noising from contributing bio-signals improving soundness of features extracted
Issues Choice of optimization algorithm and objective function for ICA
Motivation Selecting of appropriate task features and classifying machine
Issues Comparative study of perceptive (stereopsis) and non-perceptive (mental arithmetic) tasks scope of rejection in classifier
Module I Acquisition SystemObjective To develop a real time EEG acquisition system
Impedance Matching Clamping Diodes for user safety Protection Circuit
SolutionDesign Issue
Sallen Key Low Pass filter (70Hz 40dBdecade)
Digitization using PCL-HG818 card
(200Hz pacer trigger 5Vp-p)
Digitization and Anti-aliasing
Tunable Q Twin Tee Notch with feedbackLine noise suppression
Two stage Sallen Key High Pass filter
(01Hz -80dBdecade)
DC Drift accumulation at electrodes
Prevention of saturation
Two stage Amplifier total gain 10000 Stage 1 IA (AD521) Gain 25 CMRR 100dB Stage 2 HG non-inverting amplifier (~ 500)
Differential Gain
9V
9V
11
13
10
14
2
1
3
127
To DAC
5k
5k
Rg
Rs
R1 R2
R3
C3
C1 C2
100k
C1 C2
R1
R2
R3 R4
R1
C1 C2R2
R3 R4
R3R4
R1
C1
C2
R2
Instrumentation Amplifier AD521
Key Formulae Vout = Ad Vd + Acm Vcm
Component values Rs = 100kRg = 10k
Circuit parameters Gain RsRg = 10CMRR 10
Twin T High Q Notch Filter
Key Formulae Fcutoff = 1(2piRC)
Component Values R1 = R2 = 2R3 = R = 3183 kC1 = C2 = C32 = C = 1uF
Circuit Parameters Gain = 1Fcutoff = 50 HzRoll of =
2 Stage Sallen Key High pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 68kC1 = C2 = C = 22uF
Circuit Parameters Gain = 15861586 for maximally flat responseFcutoff = 1 HzRoll of = -80dBdecade
Single Stage Sallen Key Low pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 137kC1 = C2 = C = 01uF
Circuit Parameters Gain = 1586 for maximally flat responseFcutoff = 70 HzRoll of = -40dBdecade
Single Stage Amplification
Component Values R1 = 100k POTR2 = 100 ohms
Circuit Parameters Gain = 0 to 1000
AD521741
741
741 741
741741
Instrumentation Amplifier Twin T High Q Notch Filter 2 Stage Sallen Key High Pass Filter
Sallen Key Low Pass FilterAmplification stage
EEG Acquisition System
Driven Right Leg Circuit High CMRR Shielded probes
FET based instrumentation amplifiers Differential amplifiers have high input impedance Negligible input bias current Low amplifier noise
De-noising using software Discrete Filtering Independent Component Analysis (ICA)
Future Work
Objective To design experimental tasks that maximize uniqueness of features extracted
What are the desired characteristics
bull Must be easy to perform for user
bull Designing universal protocol for time invariability1048766 standardized lighting conditions1048766 gaze localization1048766 texture invariance1048766 body posture constancy
Module II Design of Experiments
StereopsisWhat is Stereopsis
bull Computation of visual depth from retinal binocular disparitybull Signals from each retina reach the visual cortex via independent pathways
Why Stereopsis
bull Highly cognitive task involving multi-sensor integration
bull Binocular fusion shows a higher FFT activity as observed from electrode P4 in MEG recordings
bull Pre-cognitive perceptive task and hence resulting EEG patterns are relatively immune to voluntary distortion by un co-operative users
Hypothesis May provide better features
Stereopsishellip
Courtesy U Shahani et al Neuroscience Letters 315 (2001) 154ndash158
StereopsishellipMEG Results from Literature
Stereopsishellipbull Verification of MEG results bull EEG recordings at IIT Guwahatibull Stereograms are used to eliminate monocular cues from depth
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Modular Flow
Motivation Development of a real time system
Issues Amplification Digitization De-noising
Motivation De-noising from contributing bio-signals improving soundness of features extracted
Issues Choice of optimization algorithm and objective function for ICA
Motivation Selecting of appropriate task features and classifying machine
Issues Comparative study of perceptive (stereopsis) and non-perceptive (mental arithmetic) tasks scope of rejection in classifier
Module I Acquisition SystemObjective To develop a real time EEG acquisition system
Impedance Matching Clamping Diodes for user safety Protection Circuit
SolutionDesign Issue
Sallen Key Low Pass filter (70Hz 40dBdecade)
Digitization using PCL-HG818 card
(200Hz pacer trigger 5Vp-p)
Digitization and Anti-aliasing
Tunable Q Twin Tee Notch with feedbackLine noise suppression
Two stage Sallen Key High Pass filter
(01Hz -80dBdecade)
DC Drift accumulation at electrodes
Prevention of saturation
Two stage Amplifier total gain 10000 Stage 1 IA (AD521) Gain 25 CMRR 100dB Stage 2 HG non-inverting amplifier (~ 500)
Differential Gain
9V
9V
11
13
10
14
2
1
3
127
To DAC
5k
5k
Rg
Rs
R1 R2
R3
C3
C1 C2
100k
C1 C2
R1
R2
R3 R4
R1
C1 C2R2
R3 R4
R3R4
R1
C1
C2
R2
Instrumentation Amplifier AD521
Key Formulae Vout = Ad Vd + Acm Vcm
Component values Rs = 100kRg = 10k
Circuit parameters Gain RsRg = 10CMRR 10
Twin T High Q Notch Filter
Key Formulae Fcutoff = 1(2piRC)
Component Values R1 = R2 = 2R3 = R = 3183 kC1 = C2 = C32 = C = 1uF
Circuit Parameters Gain = 1Fcutoff = 50 HzRoll of =
2 Stage Sallen Key High pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 68kC1 = C2 = C = 22uF
Circuit Parameters Gain = 15861586 for maximally flat responseFcutoff = 1 HzRoll of = -80dBdecade
Single Stage Sallen Key Low pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 137kC1 = C2 = C = 01uF
Circuit Parameters Gain = 1586 for maximally flat responseFcutoff = 70 HzRoll of = -40dBdecade
Single Stage Amplification
Component Values R1 = 100k POTR2 = 100 ohms
Circuit Parameters Gain = 0 to 1000
AD521741
741
741 741
741741
Instrumentation Amplifier Twin T High Q Notch Filter 2 Stage Sallen Key High Pass Filter
Sallen Key Low Pass FilterAmplification stage
EEG Acquisition System
Driven Right Leg Circuit High CMRR Shielded probes
FET based instrumentation amplifiers Differential amplifiers have high input impedance Negligible input bias current Low amplifier noise
De-noising using software Discrete Filtering Independent Component Analysis (ICA)
Future Work
Objective To design experimental tasks that maximize uniqueness of features extracted
What are the desired characteristics
bull Must be easy to perform for user
bull Designing universal protocol for time invariability1048766 standardized lighting conditions1048766 gaze localization1048766 texture invariance1048766 body posture constancy
Module II Design of Experiments
StereopsisWhat is Stereopsis
bull Computation of visual depth from retinal binocular disparitybull Signals from each retina reach the visual cortex via independent pathways
Why Stereopsis
bull Highly cognitive task involving multi-sensor integration
bull Binocular fusion shows a higher FFT activity as observed from electrode P4 in MEG recordings
bull Pre-cognitive perceptive task and hence resulting EEG patterns are relatively immune to voluntary distortion by un co-operative users
Hypothesis May provide better features
Stereopsishellip
Courtesy U Shahani et al Neuroscience Letters 315 (2001) 154ndash158
StereopsishellipMEG Results from Literature
Stereopsishellipbull Verification of MEG results bull EEG recordings at IIT Guwahatibull Stereograms are used to eliminate monocular cues from depth
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Module I Acquisition SystemObjective To develop a real time EEG acquisition system
Impedance Matching Clamping Diodes for user safety Protection Circuit
SolutionDesign Issue
Sallen Key Low Pass filter (70Hz 40dBdecade)
Digitization using PCL-HG818 card
(200Hz pacer trigger 5Vp-p)
Digitization and Anti-aliasing
Tunable Q Twin Tee Notch with feedbackLine noise suppression
Two stage Sallen Key High Pass filter
(01Hz -80dBdecade)
DC Drift accumulation at electrodes
Prevention of saturation
Two stage Amplifier total gain 10000 Stage 1 IA (AD521) Gain 25 CMRR 100dB Stage 2 HG non-inverting amplifier (~ 500)
Differential Gain
9V
9V
11
13
10
14
2
1
3
127
To DAC
5k
5k
Rg
Rs
R1 R2
R3
C3
C1 C2
100k
C1 C2
R1
R2
R3 R4
R1
C1 C2R2
R3 R4
R3R4
R1
C1
C2
R2
Instrumentation Amplifier AD521
Key Formulae Vout = Ad Vd + Acm Vcm
Component values Rs = 100kRg = 10k
Circuit parameters Gain RsRg = 10CMRR 10
Twin T High Q Notch Filter
Key Formulae Fcutoff = 1(2piRC)
Component Values R1 = R2 = 2R3 = R = 3183 kC1 = C2 = C32 = C = 1uF
Circuit Parameters Gain = 1Fcutoff = 50 HzRoll of =
2 Stage Sallen Key High pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 68kC1 = C2 = C = 22uF
Circuit Parameters Gain = 15861586 for maximally flat responseFcutoff = 1 HzRoll of = -80dBdecade
Single Stage Sallen Key Low pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 137kC1 = C2 = C = 01uF
Circuit Parameters Gain = 1586 for maximally flat responseFcutoff = 70 HzRoll of = -40dBdecade
Single Stage Amplification
Component Values R1 = 100k POTR2 = 100 ohms
Circuit Parameters Gain = 0 to 1000
AD521741
741
741 741
741741
Instrumentation Amplifier Twin T High Q Notch Filter 2 Stage Sallen Key High Pass Filter
Sallen Key Low Pass FilterAmplification stage
EEG Acquisition System
Driven Right Leg Circuit High CMRR Shielded probes
FET based instrumentation amplifiers Differential amplifiers have high input impedance Negligible input bias current Low amplifier noise
De-noising using software Discrete Filtering Independent Component Analysis (ICA)
Future Work
Objective To design experimental tasks that maximize uniqueness of features extracted
What are the desired characteristics
bull Must be easy to perform for user
bull Designing universal protocol for time invariability1048766 standardized lighting conditions1048766 gaze localization1048766 texture invariance1048766 body posture constancy
Module II Design of Experiments
StereopsisWhat is Stereopsis
bull Computation of visual depth from retinal binocular disparitybull Signals from each retina reach the visual cortex via independent pathways
Why Stereopsis
bull Highly cognitive task involving multi-sensor integration
bull Binocular fusion shows a higher FFT activity as observed from electrode P4 in MEG recordings
bull Pre-cognitive perceptive task and hence resulting EEG patterns are relatively immune to voluntary distortion by un co-operative users
Hypothesis May provide better features
Stereopsishellip
Courtesy U Shahani et al Neuroscience Letters 315 (2001) 154ndash158
StereopsishellipMEG Results from Literature
Stereopsishellipbull Verification of MEG results bull EEG recordings at IIT Guwahatibull Stereograms are used to eliminate monocular cues from depth
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
9V
9V
11
13
10
14
2
1
3
127
To DAC
5k
5k
Rg
Rs
R1 R2
R3
C3
C1 C2
100k
C1 C2
R1
R2
R3 R4
R1
C1 C2R2
R3 R4
R3R4
R1
C1
C2
R2
Instrumentation Amplifier AD521
Key Formulae Vout = Ad Vd + Acm Vcm
Component values Rs = 100kRg = 10k
Circuit parameters Gain RsRg = 10CMRR 10
Twin T High Q Notch Filter
Key Formulae Fcutoff = 1(2piRC)
Component Values R1 = R2 = 2R3 = R = 3183 kC1 = C2 = C32 = C = 1uF
Circuit Parameters Gain = 1Fcutoff = 50 HzRoll of =
2 Stage Sallen Key High pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 68kC1 = C2 = C = 22uF
Circuit Parameters Gain = 15861586 for maximally flat responseFcutoff = 1 HzRoll of = -80dBdecade
Single Stage Sallen Key Low pass filter
Key Formulae Fcutoff = 1(2piRC)Passband gain = (R3 + R4)R3
Component Values R1 = R2 = R = 137kC1 = C2 = C = 01uF
Circuit Parameters Gain = 1586 for maximally flat responseFcutoff = 70 HzRoll of = -40dBdecade
Single Stage Amplification
Component Values R1 = 100k POTR2 = 100 ohms
Circuit Parameters Gain = 0 to 1000
AD521741
741
741 741
741741
Instrumentation Amplifier Twin T High Q Notch Filter 2 Stage Sallen Key High Pass Filter
Sallen Key Low Pass FilterAmplification stage
EEG Acquisition System
Driven Right Leg Circuit High CMRR Shielded probes
FET based instrumentation amplifiers Differential amplifiers have high input impedance Negligible input bias current Low amplifier noise
De-noising using software Discrete Filtering Independent Component Analysis (ICA)
Future Work
Objective To design experimental tasks that maximize uniqueness of features extracted
What are the desired characteristics
bull Must be easy to perform for user
bull Designing universal protocol for time invariability1048766 standardized lighting conditions1048766 gaze localization1048766 texture invariance1048766 body posture constancy
Module II Design of Experiments
StereopsisWhat is Stereopsis
bull Computation of visual depth from retinal binocular disparitybull Signals from each retina reach the visual cortex via independent pathways
Why Stereopsis
bull Highly cognitive task involving multi-sensor integration
bull Binocular fusion shows a higher FFT activity as observed from electrode P4 in MEG recordings
bull Pre-cognitive perceptive task and hence resulting EEG patterns are relatively immune to voluntary distortion by un co-operative users
Hypothesis May provide better features
Stereopsishellip
Courtesy U Shahani et al Neuroscience Letters 315 (2001) 154ndash158
StereopsishellipMEG Results from Literature
Stereopsishellipbull Verification of MEG results bull EEG recordings at IIT Guwahatibull Stereograms are used to eliminate monocular cues from depth
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Driven Right Leg Circuit High CMRR Shielded probes
FET based instrumentation amplifiers Differential amplifiers have high input impedance Negligible input bias current Low amplifier noise
De-noising using software Discrete Filtering Independent Component Analysis (ICA)
Future Work
Objective To design experimental tasks that maximize uniqueness of features extracted
What are the desired characteristics
bull Must be easy to perform for user
bull Designing universal protocol for time invariability1048766 standardized lighting conditions1048766 gaze localization1048766 texture invariance1048766 body posture constancy
Module II Design of Experiments
StereopsisWhat is Stereopsis
bull Computation of visual depth from retinal binocular disparitybull Signals from each retina reach the visual cortex via independent pathways
Why Stereopsis
bull Highly cognitive task involving multi-sensor integration
bull Binocular fusion shows a higher FFT activity as observed from electrode P4 in MEG recordings
bull Pre-cognitive perceptive task and hence resulting EEG patterns are relatively immune to voluntary distortion by un co-operative users
Hypothesis May provide better features
Stereopsishellip
Courtesy U Shahani et al Neuroscience Letters 315 (2001) 154ndash158
StereopsishellipMEG Results from Literature
Stereopsishellipbull Verification of MEG results bull EEG recordings at IIT Guwahatibull Stereograms are used to eliminate monocular cues from depth
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Objective To design experimental tasks that maximize uniqueness of features extracted
What are the desired characteristics
bull Must be easy to perform for user
bull Designing universal protocol for time invariability1048766 standardized lighting conditions1048766 gaze localization1048766 texture invariance1048766 body posture constancy
Module II Design of Experiments
StereopsisWhat is Stereopsis
bull Computation of visual depth from retinal binocular disparitybull Signals from each retina reach the visual cortex via independent pathways
Why Stereopsis
bull Highly cognitive task involving multi-sensor integration
bull Binocular fusion shows a higher FFT activity as observed from electrode P4 in MEG recordings
bull Pre-cognitive perceptive task and hence resulting EEG patterns are relatively immune to voluntary distortion by un co-operative users
Hypothesis May provide better features
Stereopsishellip
Courtesy U Shahani et al Neuroscience Letters 315 (2001) 154ndash158
StereopsishellipMEG Results from Literature
Stereopsishellipbull Verification of MEG results bull EEG recordings at IIT Guwahatibull Stereograms are used to eliminate monocular cues from depth
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
StereopsisWhat is Stereopsis
bull Computation of visual depth from retinal binocular disparitybull Signals from each retina reach the visual cortex via independent pathways
Why Stereopsis
bull Highly cognitive task involving multi-sensor integration
bull Binocular fusion shows a higher FFT activity as observed from electrode P4 in MEG recordings
bull Pre-cognitive perceptive task and hence resulting EEG patterns are relatively immune to voluntary distortion by un co-operative users
Hypothesis May provide better features
Stereopsishellip
Courtesy U Shahani et al Neuroscience Letters 315 (2001) 154ndash158
StereopsishellipMEG Results from Literature
Stereopsishellipbull Verification of MEG results bull EEG recordings at IIT Guwahatibull Stereograms are used to eliminate monocular cues from depth
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Why Stereopsis
bull Highly cognitive task involving multi-sensor integration
bull Binocular fusion shows a higher FFT activity as observed from electrode P4 in MEG recordings
bull Pre-cognitive perceptive task and hence resulting EEG patterns are relatively immune to voluntary distortion by un co-operative users
Hypothesis May provide better features
Stereopsishellip
Courtesy U Shahani et al Neuroscience Letters 315 (2001) 154ndash158
StereopsishellipMEG Results from Literature
Stereopsishellipbull Verification of MEG results bull EEG recordings at IIT Guwahatibull Stereograms are used to eliminate monocular cues from depth
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Courtesy U Shahani et al Neuroscience Letters 315 (2001) 154ndash158
StereopsishellipMEG Results from Literature
Stereopsishellipbull Verification of MEG results bull EEG recordings at IIT Guwahatibull Stereograms are used to eliminate monocular cues from depth
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Stereopsishellipbull Verification of MEG results bull EEG recordings at IIT Guwahatibull Stereograms are used to eliminate monocular cues from depth
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Verification using EEGhellip
Signals from parietal region
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Verification using EEGhellip
Signals from occipital region
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Verification using EEGhellip
Sum of STFT over windows (Σ log|X(n k)|)
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
EEG Signals acquired during Stereopsis Task for four subjects
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
FFTs of EEG Signals acquired during Stereopsis Task for four subjects
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Experiment 1 A Perceptive TaskBinocular viewings of lsquoCyclopeanrsquo Wallpaper images (using industrial BIOPAC system at IIT Guwahati)3 subjects 1 trial 1 minute recordings sampledat 200Hz each of fused (vs) non-fused viewingsfrom O1 O2 P3 P4 regions
Experiment 2 A Non-perceptive TaskMental Arithmetic (CSU Dataset) 3 subjects 10 trials 10 second recordings sampled at 250Hz each from C3C4 O1 O2 P3 P4 regions
Why Mental ArithmeticTo compare perceptive and non-perceptive computationally complex tasks
Subject Identification Experiments
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
bull Design and conduct binaural perception experimentsbull Controlled environments for data collection
Future Work
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Objective To extract features most relevant to given task and optimize on the parameters of classifiers to maximize accuracy
Module III Feature Extraction and Classification
What are the desired characteristics
bull Feature Extraction
Parametric and Non- parametric (Accuracy Vs Task Invariance) Time Complexity
bull Classification
Reject option degree of accuracy Adaptive to dataset augmentation Real time implementable
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
bull Features used channel wise PSD AR Model Non-parametric FFT peaks
bull Classifiers used LVQ Fuzzy ART
Results
Task I Eye closed M Poulous 1999 80-100
Task II Viewing of standardized images R Palaniappan K V R Ravi Dec 20039418
Literature Survey
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Features used Window length = 1second Overlap size = frac12 second70 Linear Prediction Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 70 times 4 = 280bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 70 times 6 = 420bull 100 are used for training and 100 for testing
Feature Extraction
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Determination of LP order for Mental Arithmetic task
Prediction error converged at p = 6 in Levinson ndash Durbin Algorithm
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
LP order 15 and 25 are found to be reasonable guesses for Stereopsis Task
Determination of LP order for Stereopsis Task
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Classifiers used
bull Multi-class SVM with Votingbull 2 stage KNN - SVMbull 2 stage Weighted KNN - SVM
Empirically determined parameters
bull K = 14 nearest neighborsbull RBF Kernel Function with σ = 05
Classification scheme
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Results (with LPC 70 per channel)
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 1785 - 2051 -
Scheme II 22561852 16531398 1966 1930
Scheme III 28281751 17041345 1624 1495
Three Class Problem
TASK Arithmetic task Stereopsis
SchemeError Without Rejection With Rejection Without Rejection With Rejection
Scheme I 2374 - 2115 -
Scheme II 22982399 1604191 2308 1931
Scheme III 252172 1457162 2051 1620
Four Class Problem
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
bull LPCCs are weighted average of LPCs More consistent
bull LPCC have been found to give better biometric features
bull Lower dimensionality of feature space
bull Hence search space decreases computationally faster
LPC (vs) LPCC
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Features used Window length = 1second Overlap size = frac12 second6 Linear Prediction Cepstral Coefficients per window
Description of feature spaceStereopsis experiment For each subjectbull 60 seconds of data from 4 channels per subjectbull 120 patterns per subjectbull Each of dimensionality 6 times 6 = 24bull 80 are used for training and 40 for testing
Mental-Arithmetic experimentFor each subjectbull 100 seconds of data from 6 channelsbull 200 patterns bull Each dimensionality 6 times 6 = 36bull 100 are used for training and 100 for testing
Feature Extraction
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Determination of k for Weighted K-NN
K = 14 was found to give least error on a bootstrapped dataset
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Results (with LPCC)
342 427 707Weighted K-NN
With LP order = 25 15With LP order = 6SchemeError
StereopsisArithmetic taskTASK
Three Class Problem
641 833 1288 Weighted K-NN
With LP order = 25 15With LP order = 6
SchemeError
StereopsisArithmetic taskTASK
Four Class Problem
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Mental Arithmetic taskbull Within class accuracies for 4-person set with LPCC vary between 87 to 99bull Within class accuracies for 3-person set with LPCC vary between 88 to 100bull Overall accuracies for 4-person set with LPCC reach upto 88bull Overall accuracies for 3-person set with LPCC reach upto 93
Stereopsis Task bull Within class accuracies for 4-person set with LPCC vary between 87 to 100bull Within class accuracies for 3-person set with LPCC vary between 93 to 98bull Overall accuracies for 4-person set with LPCC reach upto 94bull Overall accuracies for 3-person set with LPCC reach upto 97
Summary
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
Future Work
bull Use of non-linear models for feature extraction
bull One against all scheme + High accuracy in 2 class problem ~ 96) - Re-training of all SVMs for new entry
bull Ensemble of k-means clusters Handles non-uniform distribution of training set
