brain-computer interface (bci) in a motor imagery paradigm

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.


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.


Outline•BCI Definition and Structure•Neurophysiology of Motor Tasks•Methods

▫Experimental Setup▫Band Power Features▫PLF Features▫Classification

•Experimental Results•Conclusions


BCI Definition and Structure


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.


BCI Structure


Signal Acquisition• Measures of brain activity:

▫ Electrophysiological signals: EEG; ECoG; Intracortical devices.

▫ Magnetic systems: MEG.

▫ Metabolic measures: fMRI; NIRS.


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


Feature Classification•Classify features according to the

experimental paradigm.

•Typical algorithms:▫Neural Networks;▫Linear Discriminant Analysis;▫Fisher’s Discriminant Analysis;▫Support Vector Machine (SVM).


Applications• Control of assistive

technologies:▫ Communication;▫ Environment control;▫ Locomotion;▫ Gaming and virtual

reality.

• Neurorehabilitation.


Neurophysiology of Motor Tasks


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


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.


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.


Methods


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:


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)


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


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)


Classification• Support Vector Machines

(SVMs) used in a hierarchical structure;

• SVMs trained with a gaussian kernel;

• Results evaluated with Leave-One-Out Cross Validation;


Experimental Results


EEG Signal• Imagination of right hand movement:


Comparison of Features

Imagination of right hand movement.

Band Power Features

(BPF)

PLF Features(PLFF)


Classification ResultsActual Movement Imagined Movement

AverageBPF: 68.67 %PLFF: 86.58 %

AverageBPF: 71.86 %PLFF: 86.34 %


Conclusions


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.


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.


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.


Thank You!

Acknowledgements The 6 subjects who volunteered their

brainsCENC and Prof. Teresa Paiva

David Belo

brain-computer interface (bci) in a motor imagery paradigm

Documents

bci structurecarlos

bci definitiona bci

motor function

electroencephalogram

different motor tasks

imagination of motor

almeida motivationa

bci development of bcis