Haptics

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2014

Haptics

Symposium

2014

Exoskeleton control by Motor Imagery

BCI for upper limb neurorehabilitation

Michele Barsotti, Antonio Frisoli

m.barsotti@sssup.ita.frisoli@sssup.it

Brain Computer Interfaces

&

Haptics

February 23-26, 2014,

Houston TexasHaptics Symposium

2014

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• Brain activity acquisition methods

• General Brain Computer Interfaces

• Brain anatomy

• Movements EEG correlates

• Decoding movement intention byanalyzing EEG

• Feedbacks for motor-imagery-BCI

• Implementing a MI-BCI paradigm

OUTLINE

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BRAIN ACTIVITY ACQUISITION

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ACQUIRING BRAIN ACTIVITY

Temporal Resolution [s]

Sp

ati

al

Reso

luti

on

[cm

]

BASED ON THE

BLOOD FLOW

VARIATION

BASED ON THE

MAGNETIC-

ELECTRICAL

ACTIVITY

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ACQUIRING BRAIN ACTIVITY

Temporal Resolution [s]

Sp

ati

al

Reso

luti

on

[cm

]

ElectroCorticoGraphy

(ECoG)

Very good spatial and

temporal resolution (firing

of a single neuron)

INVASIVE

Surgical intervention

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ACQUIRING BRAIN ACTIVITY

Temporal Resolution [s]

Sp

ati

al

Reso

luti

on

[cm

]

• Most widely used strategy for BCI applications

• Good Temporal

Resolution

• Several portable, cheap systems exist

•Motion artifacts and interferences can be greatly reduced by employing active electrodes

EEG is the record of

electrical activity of brain by

placing the electrodes on

the scalp.

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EEG SIGNALs FEATURES

AMPLITUDE RANGE:

Wake EEG:: Vpp = 100µV

Sleep EEG: Vpp = 300µV

FREQUENCY RANGE:

From 0.01 to 100 Hz

COMMON EEG ARTIFACTs:

Eye blinking (eye movement)

Muscular activity (EMG)

Ambient Noise + (50Hz-60Hz)

Electrodes Movement

Zero Mean

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NATURAL EEG RHITMIC ACTIVITYBand [Hz] Normaly Location

Gamma 32 + Displays during cross-modal sensory

processing and short-term memory

Somatosensory cortex

Beta 16 -

32

active thinking, focus, hi alert,

anxious

both sides, symmetrical

distribution, most evident

frontally; low-amplitude waves

Alpha 8 -

16

relaxed/reflecting

closing the eyes

inhibition control

posterior regions of head, both

sides, higher in amplitude on

non-dominant side.

Mu 8 -

12

Shows rest-state motor neurons Sensorimotor cortex

Theta 4 - 8 higher in young children

drowsiness in adults and teens

idling

Found in locations not related to

task at hand

Delta up to

4

adult slow-wave sleep

Has been found during some

continuous-attention tasks

frontally in adults, posteriorly in

children; high-amplitude waves

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NATURAL EEG RHITMIC ACTIVITYBand [Hz] Normaly Location

Gamma 32 + Displays during cross-modal sensory

processing and short-term memory

Somatosensory cortex

Beta 16 -

32

active thinking, focus, hi alert,

anxious

both sides, symmetrical

distribution, most evident

frontally; low-amplitude waves

Alpha 8 -

16

relaxed/reflecting

closing the eyes

inhibition control

posterior regions of head, both

sides, higher in amplitude on

non-dominant side.

Mu 8 -

12

Shows rest-state motor neurons Sensorimotor cortex

Theta 4 - 8 higher in young children

drowsiness in adults and teens

idling

Found in locations not related to

task at hand

Delta up to

4

adult slow-wave sleep

Has been found during some

continuous-attention tasks

frontally in adults, posteriorly in

children; high-amplitude waves

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GENERAL BRAIN COMPUTER

INTERFACES

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GENERAL BCI FRAMEWORK

SIGNAL PROCESSING

FEATURES EXTRACTION

1 CSP

FC3 FCZ FC4

C5 C3 C1 CZ C2 C4 C6

CP3 CPZ CP4

-0.2 -0.1 0 -0.2 -0.1 0

CSP

13 CSP

FC3 FCZ FC4

C5 C3 C1 CZ C2 C4 C6

CP3 CPZ CP4VAR

MAXVAR

MIN

SIGNAL CLASSIFICATION

APPLICATION OUTPUT

BIOFEEDBACK

USER MENTAL

STRATEGY

BRAIN SIGNALs

ACQUISITION

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Feedback for

subject training

Machine

learning

BCIs represent a set of techniques to allow direct control of a software or device via brain activity – without the need of a motor output

The most common BCI approach exploits voluntary modulation of EEG activity, although more invasive approaches have been explored

These techniques have successfully been employed to aid disabled patients

Recently BCIs have also been investigated as a rehabilitation tool

GENERAL BCI FRAMEWORK

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BCI CATEGORIES

INVASIVE NON-INVASIVE

Without penetrating the

skalp, mostly EEG, rarely

magnetoencephalogram

(MEG) or functional

magnetic resonance

imaging fMRI

- Several portable, cheap systems

exist

- Motion artifacts and interferences

can be greatly reduced by employing

active electrodes

DEPENDING ON THE ACQUISITION SYSTEM

Implanted sensors

(electrode array, needle

electrodes,

electrocorticogram ECoG)

-Control of 2-3 DoF, with good

accuracy.

-Implants have only been tested for

months after surgery

--Highly expensive

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BCI

Invasive Non invasive

Single recording site

Multiple recording sites

ECoG

EEG

MEG

fMRI

Classification: signal acquisition

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Insertion of arrays of microelectrodes in cortical tissue

Control of 2-3 DoF, with good accuracy.

Implants have only been tested for months

after surgery

Highly expensiveHochberg et al., Nature, 2006

Invasive vs. non-invasive BCI

Invasive BCI

Non-invasive BCI

EEG systems range from low to high density (2 to 256 eletrodes)

Several portable, cheap systems exist

Motion artifacts and interferences can be

greatly reduced by employing active electrodes

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BCI CATEGORIES

INDEPENDENT DEPENDENT

A Dependent BCI does not

use the brain’s normal

output pathways to carry

the message, but activity in

these pathway is needed to

generate the brain activity

that does carry it.

Independent from

peripheral nerves and

muscles, using only

central nervous system

(CNS) activity

DEPENDING ON THE MENTAL STRATEGY

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BCI CATEGORIES

ENDOGENOUS EXOGENOUS

Evoked Potentials:

Users modulate brain

responses to external

stimuli

SSVEP

p300

Unstimulated Brain

Signals:

Users can voluntarily

produce the required

signals

(Motor Imagery,

Computational Task)

DEPENDING ON THE MENTAL STRATEGY

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BCI CATEGORIES

ASYNCHRONOUS

Commands can only be

emitted synchronously with

external pace.

The system detects

when the user wants to

emit a command

DEPENDING ON THE COMMAND-TIMING

SYNCHRONOUS

The differences in EEG response following different stimuli are

used to discriminate what subjects want

Subjects are asked to perform visual imagery tasks and

the local changes in EEG power spectra are recorded

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SSVEP

VEP

MOTOR IMAGERY ERP (i.e.P300)

BCI CATEGORIES - SUMMARY

EXOGENOUSENDOGENOUSD

EPEN

DEN

TIN

DEPEN

DEN

T

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BRAIN ANATOMY & EEG MOVEMENTS

CORRELATES

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BRAIN ANATOMY

[Martini, 2006]

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The Primary Somatic Sensory Cortex (Parietal Lobe) and the Primary Motor

Cortex (Temporal Lobe) are the most important regions for BCI research.

I

III

IV V

IIBRAIN ANATOMY: THE CEREBRAL CORTEX

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The Primary Somatic Sensory Cortex (Parietal Lobe) and the Primary Motor

Cortex (Temporal Lobe) are the most important regions for BCI research.

I

III

IV V

IIBRAIN ANATOMY: THE CEREBRAL CORTEX

M1 S1

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The Primary Somatic Sensory Cortex (Parietal Lobe) and the Primary Motor

Cortex (Temporal Lobe) are the most important regions for BCI research.

I

III

IV V

IIBRAIN ANATOMY: THE CEREBRAL CORTEX

M1 S1

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TYPES OF MOVEMENTThree types of movements may occur in respect of to ascending

and descending signals via different pathways and at different

levels: • Reflexes movement : are performed

subconsciously and can occur at an exclusively

spinal level

• Rhythmic movement: stereotyped action

involving repetitions of the same movements

The control is at the spinal level without

involvement of higher cortical control

• Voluntary movement: usually goal directed

and therefore fully conscious. It arises in the

motor cortex and is executed by the spinal

cord.

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TYPES OF MOVEMENTThree types of movements may occur in respect of to ascending

and descending signals via different pathways and at different

levels: • Reflexes movement : are performed

subconsciously and can occur at an exclusively

spinal level

• Rhythmic movement: stereotyped action

involving repetitions of the same movements

The control is at the spinal level without

involvement of higher cortical control

• Voluntary movement: usually goal directed

and therefore fully conscious. It arises in the

motor cortex and is executed by the spinal

cord.

When a voluntary movement is started, neurons in the

M1 send commands to upper and lower motor neurons.

The M1 needs to be stimulated by neurons from the

premotor cortex and the supplementary motor area

(SMA), which support and coordinate the M1, in order to

initiate a voluntary movement

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Motor imagery is a mental process by which

an individual rehearses or simulates a given

action.

MOTOR IMAGERY

Performing motor imagery or attempting a

movement (i.e. for patients) influences the

brain activity as the voluntary movements do.

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Why MOTOR IMAGERY is suitable for BCI?

• No need of external stimulus (it could be asynchronous)

• Not depend in any way on the brain’s normal output/input pathways

(independent)

• Possibility to provide different commands depending on which body

part is evolved in the simulated action

• Mental practice of motor actions via BCI training affect neuro-

rehabilitation in a positive way.

• the power in μ (8-12 Hz) and β (12-24Hz) EEG rhythms are affected

by motor imagery: Event Related Spectral Perturbation (ERSP)

• Users learn to perform motor imagery tasks

• Can be employed event if the motor areas are impaired

• Works mostly for digital control, has a fast response

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DECODING MOVEMENT

INTENTIONS BY ANALIZING EEG

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Event Related Potential (ERP):

- Repeatedly present discrete stimulus, average

raw EEG responses across presentations.

Characteristic feature (eg. P300)

Event Related Spectral Perturbation (ERSP):

Frequency band changes

- Average spectral features across presentation.

- Characteristic suppression/increase in power

(ERD/ERS: Event Related De-Synchronization).

EEG PHENOMENAL USABLE FOR BCI

Event Related Spectral Perturbation (ERSP) and Event

Related Potential ERP are the measured brain response that

are the direct result of a specific sensory, cognitive, or motor

event.

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time

Fre

que

ncy [

Hz]

epochtimechannel X epochfrequencytimechannel X

AVERAGING

time

Am

plit

ude [µ

V]

ERP ERSP

• The ERPs and ERSP should be extracted from the background

noise mediating many recordings (Epochs or Trials)

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Am

plit

ude

[µV

]

EEG background

noise ~ 1/sqrt(N)

Costant Signal ERP

Repetition (N)

Post-Stimulus

EEG

Costant

Signal

Background

Noise

average

ERP

average

Signal

average

Noise

AVERAGING THEORYS/N ratio increases as a function of the square root of the number of trials.

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MOTOR IMAGERY CORRELATES IN EEGPerforming (or imagining) a motor action influences the EEG with two

main phenomena:

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SLOW CORTICAL POTENTIALS [Kornhuber and Deecke (1965) ]

• Know as BereitschaftPotential

(readiness potential) or Movement

Related Cortical Potentials (MRCPs).• Slow oscillations preceding the

movement

• Localized over the supplementary

motor area (SMA)

• Steps for MRCP detection

• Spatial filter,

• LP frequency filter

• Template extraction from the

training data

• matching with the ongoing eeg

MOTOR IMAGERY CORRELATES IN EEG

•Frequency close to the DC -> very

challenging to detect in single trial

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SENSORIMOTOR RHYTHMS [Pfurtscheller and Lopes da Silva, (1999)]

• the power in μ (8-12 Hz) and β (12-24

Hz) EEG rhythms are affected by motor

imagery.

•Know also as Event Related

De/Synchronization (ERD,ERS)

•Steps for MRCP detection

• Spatial filter,

• Band Pass frequency filter

• Feature extraction

• LDA classifier

• High average classification

accuracy (>80%)

MOTOR IMAGERY CORRELATES IN EEG

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ERD extraction: example with motor imageryCollecting Trials

from a specific

electrode

Bandpass on the

specific frequency

Squaring Signals

Averaging over

Trials

Smoothing

[Pfurtscheller and Lopes da Silva, (1999)]

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β ERD

13-30 Hz

µ ERD

8-12 Hz

Event Related

De\Synchronization

ERD Motor Imagery of

right hand

movement

EVENT RELATED SPECTRAL PERTURBATION

SENSORIMOTOR RHYTHMS

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MOTOR IMAGERY: SIGLE TRIAL DETECTIONThe important features of the motor imagery are:

The frequency band.

The spatial localization

A priori knowledgment:

The frequency band are mu (8 -13Hz) and beta (15-30 Hz).

The spatial localization is over the sensory motor

Very high intersubject

variability!

Need of optimized

spatial filters

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The aim of spatial filtering is to improve the signal-to-noise ratio by

creating a virtual channel which is a (linear, in the following cases)

combination of the input channels of the filter.

A spatial filters can optimize the data extracted from an high number of

electrodes reducing the dimension of the features'space to only few

significant dimensions.

N-channel input (ex. 16 ch) 1-optimized channel output

1 CSP

FC3 FCZ FC4

C5 C3 C1 CZ C2 C4 C6

CP3 CPZ CP4

-0.2 -0.1 0 -0.2 -0.1 0

CSP

13 CSP

FC3 FCZ FC4

C5 C3 C1 CZ C2 C4 C6

CP3 CPZ CP4

SPATIAL FILTERING

y(t) = a*ch1(t) + b*ch2(t) ....

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Optimized Spatial filter: Common Spatial Pattern – CSP

VAR MAX VAR MIN

VAR MIN

RAW

CHANNELS

FIRST AND LAST

CSP FILTER

PROJECTED

DATA

REST MOVERESTMOVE

Trial i Trial i+1 Trial i Trial i+1

VAR MAX

[Pfurtscheller 1999]

Common Spatial Pattern (CSP) is a supervised spatial filtering

method for two-class discrimination problems, which finds

directions that maximize variance for one class and at the same

time minimize variance for the other class.

1 CSP

FC3 FCZ FC4

C5 C3 C1 CZ C2 C4 C6

CP3 CPZ CP4

-0.2 -0.1 0 -0.2 -0.1 0

CSP

13 CSP

FC3 FCZ FC4

C5 C3 C1 CZ C2 C4 C6

CP3 CPZ CP4

1 CSP

FC3 FCZ FC4

C5 C3 C1 CZ C2 C4 C6

CP3 CPZ CP4

-0.2 -0.1 0 -0.2 -0.1 0

CSP

13 CSP

FC3 FCZ FC4

C5 C3 C1 CZ C2 C4 C6

CP3 CPZ CP4

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WHITENING

MATRIX

TRIALS

CLASS A

TRIALS

CLASS B

COVARIANCE

CLASS A

COVARIANCE

CLASS B

i

Ti

A

i

A

Ti

A

i

AA

XXtrace

XXR

)(

i

Ti

B

i

B

Ti

B

i

BB

XXtrace

XXR

)(

BAc RRR

T

CCCC UUR

T

CC UW1

T

AA WWRS T

BB WWRS T

AA UUS T

BB UUS

IBA

WUP T

PXZ

COMPOSITE

COVARIANCE

Transformed

Covariance A

Transformed

Covariance BEIGENVECTOR

PROJECTION

MATRIX

EIGENVALUES

Common Spatial Pattern – Algorithms

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• The scalp-plot of the Common Spatial Pattern can be also used to give a physiological interpretation of the data

Common Spatial Pattern: advantages

• Since variance of band-pass filtered signals is equal to band-power, CSP filters are well suited to discriminate mental states characterized by spectral perturbations (ERD and motor imagery based BCIs).

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The log-scaled band-power values in the mu and beta band of

the resulting two projected channels, can be used as a two-

dimensional feature of the brain activity. Classification is

performed using a linear discriminant classifier (LDA) or a

support vector machine (SVM)

CLASSIFICATION

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CSP VARIANTS

CSP – Pfurtscheller 1998

FWM – Liu 2010

CSSSP – Blankertz 2006

CSSP – Lemm 2005

SPEC-CSP – Tomioka 2006

SB-CSP – Novi 2008

FB-CSP – Ang 2008

dCSP – Wang 2010

SSCSP – Arvaneh 2011

I-CSP – Blankertz 2008

With

Frequency

Optimization

Furhter Spatial

Optimization

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FEEDBACKs for

motor imagery -

BCI

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FEEDBACK FOR MOTOR IMAGERYThe biofeedback provided as a response to the mental activity can

improves the usability of motor imagery BCI.

The congruency of the provided feedback with the mental task is

expected to ease the performance of motor imagery.

Game

Illusion

Virtual reality

Exoskeleton

VISUAL

PROPRIOCEPTIVE

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motor imagery –

BCI in

neurological

rehabilitation

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BCI in neurological rehabilitation

Daly & Wolpaw, Lancet, 2008

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Daly & Wolpaw, Lancet, 2008Goal: The subject should be able to control

muscle activity through brain activity

BCI in neurological rehabilitation

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Daly & Wolpaw, Lancet, 2008

Strategy 1: Train subjects to modulate brain activity via visualization and voluntary control of relevant features

BCI in neurological rehabilitation

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Daly & Wolpaw, Lancet, 2008

Strategy 2: Train subjects by using brain activity to aid motion with assistive devices

BCI in neurological rehabilitation

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A new multimodal architecture for gaze-independent brain–computer

interface (BCI)-driven control of a robotic upper limb exoskeleton for

stroke rehabilitation to provide active assistance in the execution of

reaching tasks in a real setting scenario.

Object

1

Object

2

Work

plane

Kinect

Eye tracker

BC

I

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VAR

MIN

OPTIMAL CHANNELSMOVE

VAR

MAX

REST MOVE REST

VAR

MAX

VAR

MIN

ORIGINAL CHANNELSCSP

FILTERS

SVM

CLASSIFIER

TRAINING PHASE

VISUAL CONDITION ROBOT CONDITION

Involving the BCI module only and

the visual feedback of a virtual arm

controlled through motor

The subject performed a test session

with the complete system:

Kinect – EyeTracker – BCI – ArmExos

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BCI-REHABILITATION PROTOCOL

BCIEEG acquisition

& processing

L-EXOSproprioceptive

feedback

MONITORvisual feedback

• TRAINING PHASE: visual and proprioceptive feedback are provided accordingly to the task

• EXERCISE PHASE: the real-time classification output of the BCI was used for driving the

proprioceptive and visual feedback

ALL PATIENTS WERE ABLE TO CONTROL THE

BCI SYSTEM AFTER THE FIRST TWO

SESSION

5 right hemiparetic stroke

patients enrolled

SESSION STRUCTURE:

• MOVEMENT: the patient have to perform motor imagery of his impaired arm

• REST: the patient have to hold a resting mental state

TASKs REQUIRED:

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BCI paradigm

based on Motor

Imagery

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EEG

acquisition

Signal filtering

and

conditioning

Features

extraction

Features

classification

Online operations:

User

Offline BCI training

Frequency

bands and

artifact

removal

parameters

Spatial

Filter

parameters

Classifier

weights

Real-time feedback

MOTOR IMAGERY BCI: WORKFLOW

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EEG CONFIGURATION

EEG channels: minimal configuration

Frontal ground electrode

Reference ear lobe electrode

Electrodes covering the motor cortex

Electrode for eye-blink detection and removal

Feature extraction

The power in the mu (8-12 Hz) and beta (16-24 Hz) bands is computed over 500 ms windows.

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TRAINING PHASE

Training paradigmSubjects are asked to perform several motor imagery trials.

1. Feature classificationAcquired data is classified into two or more classes via machine

learning techniques, to optimize feature classification

2. Subject trainingThe subject is trained again with the output of the feature classifier as a

feedback signal, in order to optimize its motion imagery

TRIAL STRUCTURE

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DATA PROCESSINGTRAINING

• Import data with the channel location

• Subdivide data into epochs for the two classes

• Remove artifactuated epochs

• Train the Common Spatial filter

• Extract Features

• Train the classifier

it is possible to predict the

BCI performance by a

visual inspection of both

the time-frequency plot of

the CSP-projected

channels and the

features plot

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DATA PROCESSING: Visual Inspection

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Time Frequency plot raw channels

Time [ms]

Fre

qu

en

cy [H

z]

C3

-2000 0 2000 4000

10

20

30

-2

0

2

Time [ms]

Fre

qu

en

cy [H

z]

CZ

-2000 0 2000 4000

10

20

30

-2

0

2

-2

0

2

CHANNELS ERD MAPS - MOVE

Time [ms]

Fre

qu

en

cy [H

z]

C4

-2000 0 2000 4000

10

20

30

Time [ms]

Fre

qu

en

cy [H

z]

C3

-2000 0 2000 4000

10

20

30

-2

-1

0

1

2

Time [ms]

Fre

qu

en

cy [H

z]

CZ

-2000 0 2000 4000

10

20

30

-2

-1

0

1

2

-2

-1

0

1

2

CHANNELS ERD MAPS - REST

Time [ms]

Fre

qu

en

cy [H

z]

C4

-2000 0 2000 4000

10

20

30

Click on electrodes to toggle name/number

Click on electrodes to toggle name/number

Click on electrodes to toggle name/number

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Time Frequency plot CSP projected channels

Time [ms]

Fre

qu

en

cy [H

z]

MOVE - First CSP

-2000 -1000 0 1000 2000 3000 4000

10

20

30

-5

0

5

Time [ms]

Fre

qu

en

cy [H

z]

MOVE - Last CSP

-2000 -1000 0 1000 2000 3000 4000

10

20

30

-2

0

2

Time [ms]

Fre

qu

en

cy [H

z]

REST - First CSP

-2000 -1000 0 1000 2000 3000 4000

10

20

30

-2

0

2

-2

0

2

Time [ms]

Fre

qu

en

cy [H

z]

REST - Last CSP

-2000 -1000 0 1000 2000 3000 4000

10

20

30

FC3 FCZ FC4

C5 C3 C1 CZ C2 C4 C6

CP3 CPZ CP4

FC3 FCZ FC4

C5 C3 C1 CZ C2 C4 C6

CP3 CPZ CP4

First CSP

First CSP

Last CSP

Last CSP

REST trials

MOVE trials

Haptics

Symposium

2014

Haptics

Symposium

2014

0 1000 2000 30000

20

40

60

80

100

Time [ms]

Co

rrect

Rate

[%

]

CLASSIFIER PERFORMANCE

'Rest' ->89.65%

'Move'->99.95%

'Total' ->95.10%

1.8 2 2.2 2.4 2.6

2

2.5

3

3.5

1st CSP - Log Features

2n

d C

SP

- L

og

Featu

res

0

1

Support Vectors

PREDICTING RESULTS

Analysis of the BCI

output calculated with

parameters extracted

from the same dataset

Plot of each trial in

the features

space

Haptics

Symposium

2014

Haptics

Symposium

2014

MODEL

EEG amp

CSP and LDA

weightsSpatial Filtering

Features ExtractionClassifier

User Interface

Haptics

Symposium

2014

Haptics

Symposium

2014

SHOWING RESULTs

[Frisoli et al. 2012]

Haptics

Symposium

2014

Haptics

Symposium

2014

email: a.frisoli@sssup.it

m.barsotti@sssup.it

thank you!