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Brain-Computer Interface (BCI) in a Motor Imagery Paradigm. Carlos Carreiras Adviser: Prof. João Sanches Co-Adviser: Prof. Luís Borges de Almeida. Motivation. A BCI attempts to provide an additional channel of communication for its users; - PowerPoint PPT Presentation
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Brain-Computer Interface (BCI) in a Motor Imagery Paradigm
Carlos Carreiras
Adviser: Prof. João SanchesCo-Adviser: Prof. Luís Borges de Almeida
Carlos Carreiras December 2011
Motivation• A BCI attempts to provide an additional channel
of communication for its users;
• A user’s intent is directly extracted from the brain and translated into commands by the BCI;
• Development of BCIs has been a very active research field in recent years;
• Important for patients that are “locked in”, as they have limited motor function.
Carlos Carreiras December 2011
Contributions• To develop an Electroencephalogram (EEG) BCI;
• The BCI is controlled through the imagination of motor tasks;
• A total of 6 different motor tasks are considered;
• The motor tasks are identified by analysing the pattern of Event-Related Desynchronization (ERD) and Synchronization (ERS) in the EEG;
• A new method to identify ERD/ERS, from the field of synchronization quantification, is propopsed.
Carlos Carreiras December 2011
Outline•BCI Definition and Structure•Neurophysiology of Motor Tasks•Methods
▫Experimental Setup▫Band Power Features▫PLF Features▫Classification
•Experimental Results•Conclusions
Carlos Carreiras December 2011
BCI Definition and Structure
Carlos Carreiras December 2011
BCI Definition• A BCI is a system that measures brain signals
and converts them into outputs;
• These outputs do not depend on the normal pathways of peripheral nerves and muscles.
• A user controls the BCI:▫By perceiving a set of stimuli and concentrating
on a certain stimulus that accomplishes the user’s intent;
▫By concentrating on a specific mental task.
Carlos Carreiras December 2011
BCI Structure
Carlos Carreiras December 2011
Signal Acquisition• Measures of brain activity:
▫ Electrophysiological signals: EEG; ECoG; Intracortical devices.
▫ Magnetic systems: MEG.
▫ Metabolic measures: fMRI; NIRS.
Carlos Carreiras December 2011
Feature Extraction• The feature extraction
method depends on the type of mental task.
• Time-Domain Features:▫ Filtering;▫ Wavelet transform;▫ AR models.
• Frequency-Domain Features:▫ Fourier analysis;▫ Morlet wavelets;▫ AR models.
Source: Bashashati et al., 2007
Carlos Carreiras December 2011
Feature Classification•Classify features according to the
experimental paradigm.
•Typical algorithms:▫Neural Networks;▫Linear Discriminant Analysis;▫Fisher’s Discriminant Analysis;▫Support Vector Machine (SVM).
Carlos Carreiras December 2011
Applications• Control of assistive
technologies:▫ Communication;▫ Environment control;▫ Locomotion;▫ Gaming and virtual
reality.
• Neurorehabilitation.
Carlos Carreiras December 2011
Neurophysiology of Motor Tasks
Carlos Carreiras December 2011
The Motor Cortex• The Primary Motor Cortex (PMC)
is responsible for planning and executing movements;
• There is a correspondence between areas of the PMC and the various muscle groups;
• Movement tasks induce changes to brain activity visible in the EEG.
Source: Guyton and Hall, 2005
Carlos Carreiras December 2011
ERD and ERS• Certain events change the oscillating dynamics of brain
waves;
• The changes are frequency-specific, and can be:▫ Decreases in power – Event-Related Desynchronization (ERD);▫ Increases in power – Event-Related Synchronization (ERS).
• Neuronal networks become asynchronous during mental activity;
• ERD – correlated with mental activity;• ERS – correlated with mental inactivity.
Carlos Carreiras December 2011
ERD and ERS• During a motor task:
▫ ERD in the corresponding region of the PMC (10 – 20 Hz);
▫ ERS over unrelated cortical areas;
▫ Post-movement ERS in the corresponding region of the PMC (13 – 25 Hz).
Source: Piotr J. Durka, 2001
Imagination and observation of motor tasks produce similar changes in the brain as actual
movement.
Carlos Carreiras December 2011
Methods
Carlos Carreiras December 2011
Experimental Setup• EEG signals acquired from 6
voluntary subjects;
• Subjects imagined various motor tasks:▫ No Movement (CC)▫ Right Foot (RF);▫ Left Foot (LF);▫ Right Leg (RL);▫ Left Leg (LL);▫ Right Hand (RH);▫ Left Hand (LH);
• Procedure:
Carlos Carreiras December 2011
Band Power Features• ERD is traditionally
measured by computing the EEG power in specific frequency bands:▫ Fourier Transform;▫ Auto-Regressive Models;▫ Continuous Wavelet
Transform.
• Disadvantages:▫ Necessary to select
frequency band (changes with subject);
▫ Indirect measure of the phenomenon.
Windowing(256 ms, 50% overlap)
EEG Channel
Compute FFT
Average Power(8 – 15 Hz)
Carlos Carreiras December 2011
Analytical Signals• An analytical signal is a
signal that has no negative-frequency components;
• Obtained by adding a quarter-cycle time shift (Hilbert Transform Filter);
• Phase obtained by: d/dt = 10 Hz
d/dt = 10 Hz
d/dt = 30 Hz
Carlos Carreiras December 2011
Phase-Locking Factor Features• During ERD, certain
neuronal networks become out of sync;
• The Phase-Locking Factor (PLF) measures the synchronization between 2 signals:
• PLF = 1 – perfect synchrony;
• PLF = 0 – no synchrony.
Analytical Signals
Compute PLF
Windowing(256 ms, 50% overlap)
Carlos Carreiras December 2011
Classification• Support Vector Machines
(SVMs) used in a hierarchical structure;
• SVMs trained with a gaussian kernel;
• Results evaluated with Leave-One-Out Cross Validation;
Carlos Carreiras December 2011
Experimental Results
Carlos Carreiras December 2011
EEG Signal• Imagination of right hand movement:
Carlos Carreiras December 2011
Comparison of Features
Imagination of right hand movement.
Band Power Features
(BPF)
PLF Features(PLFF)
Carlos Carreiras December 2011
Classification ResultsActual Movement Imagined Movement
AverageBPF: 68.67 %PLFF: 86.58 %
AverageBPF: 71.86 %PLFF: 86.34 %
Carlos Carreiras December 2011
Conclusions
Carlos Carreiras December 2011
Summary•A BCI can be controlled through the
imagination of motor tasks;
•Detection of motor tasks is done by identifying ERD in the EEG:▫Band Power Features;▫PLF Features;
•Classification is made with a hierarchical SVM classifier.
Carlos Carreiras December 2011
Summary•The system is capable of distinguishing
between 6 motor tasks.
•PLF features provide better results than the traditional band power features:▫Increase in average accuracy;▫Decreased subject variability;▫Less susceptible to noise.
Carlos Carreiras December 2011
Future Work• Improve the experimental setup:
▫ More subjects;▫ Better session procedure.
• Better understanding of PLF features:▫ Mapping;▫ Combination with other features.▫ Feature selection;
• Real-Time BCI:▫ Computational efficiency;▫ Continuous adaptation;▫ Good feedback system.
Carlos Carreiras December 2011
Thank You!
Acknowledgements The 6 subjects who volunteered their
brainsCENC and Prof. Teresa Paiva
David Belo