By

Rajdeep Chatterjee

Overview Introduction

Brain Rhythms

EEG

Motor Imagery

Data and system description

Work flow diagram

Feature extraction techniques

Classification and results

Conclusions

References

Introduction In 1924 Hans Berger[1], a German psychiatrist first

recorded brain activity by means of EEG. He also identified the alpha wave (8-13Hz) which is also called Berger’s wave after his name.

Image courtesy : http://blog.ketyov.com/2010/08/eeg-hans-berger-and-psychic-phenomena.html

Brain Rhythms Micro volt electric signals generated due to different activities in our

brain are called brain rhythms.

Depending on the frequency range brain waves or rhythms can be classified as follows :

Band Freq (Hz) Normally

Delta <4 •Adult low-wave sleep •In babies •During some continuous-attention tasks

Theta 4-7 •Higher in young children •Drowsiness in adults and teens •Idling •When trying to inhibit a response or action

Alpha 8-15 •Relaxed/reflecting •Closing eyes •Inhibition control

Beta 16-31 •Range span: active calm -> intense -> stressed -> mild obsessive •Active thinking, focus, hi alert, anxious

Brain Rhythms (Contd.)

Band Freq (Hz) Normally

Gamma 32+ •During cross-model sensory processing (i.e. When combining 2 different senses like sound and sight) •During short-term memory matching of recognized objects, sounds and tactile sensations

Mu (sensori-motor rhythm)

8-12 •Shows rest-state motor neurons

Brain Rhythms : Wave Shapes

From left-top to left-down: Delta, Theta, Alpha

From right-top to right-down: Beta, Gamma, Mu

Image courtesy : http://www.sleepwarrior.com/brain-waves-brainwave-entrainment

EEG: Electroencephalogram It is a non invasive way to

record the micro electrical activities in the brain.

Electrodes are placed at some particular positions on scalp to record the brain signals.

It has fine temporal resolution.

It is easy to record, portable and has low set-up cost.

Image courtesy : http://journal.frontiersin.org/article/10.3389/fneur.2012.00077/full

Basic Functional Brain Map

Image courtesy : http://www.diva-portal.org/smash/get/diva2:440513/FULLTEXT01.pdf

EEG : Electrode Distribution

Image courtesy : http://www.ecsjournal.org/Archive/Volume38/Issue3/5.pdf

According to IEEE 10 20 rule the 19 channel EEG electrodes should be distributed as follows:

Motor Imagery Activities Our activities like hand movement, foot movement etc. are

performed under the control of a functional region of our brain namely, motor cortex which sends the required signal to the limbs through motor neurons.

Due to these motor activities certain changes can be observed in particular brain rhythms.

Even if we think or imagine about such activities with out doing those physically, then also similar changes can be observed in the same brain rhythms.

This type of thinking or imagination based activities are known as Motor Imagery (MI) activities.

Data Description BCI competition II 2003 data set III from the Department

of Medical Informatics, Institute for Biomedical Engineering, University of Technology Graz[2].

The EEG data was sampled at 128 Hz.

Only C3 and C4 are considered for extracting information

on left-right hand movement.

We have used 140 trials with ten folds cross validation technique for each of the classifiers.

The data are filtered with a band pass filter to keep only the

frequencies in the band 0.5 Hz to 30 Hz.

System Description We have used MATLAB R2014a for feature extraction and

WEKA 3.6 classification tool to classify the obtained feature vectors in the following configuration-

Intel Core i5 computer. DDR3 4GB RAM. Windows 7 (64 bit)

Work flow diagram

Feature Extractions Feature vectors Size

(No. of trials x No. of

Features per trial)

Statistical-based 140 x 12

Wavelet-based Energy & Entropy 140 x 04

Wavelet-based RMS 140 x 02

Average Power (PSD-based) 140 x 04

Band power 140 x 04

Total 140 x 26

Classifiers Set up Classifier

Name Variant

type Kernel functions used

SVM C-SVC (C) Linear (L)

Polynomial (P)

Radial Basis (R)

Sigmoidal (S)

Nu-SVC (N) Linear (L)

Polynomial (P)

Radial Basis (R)

Sigmoidal (S)

Learning rate Momentum

MLP 0.7 0.29

Empirical Results : Area under ROC

AUC ( Left hand as positive outcome) AUC ( Right hand as positive outcome)

Empirical Results : for SVM CL CP CR CS NL NP NR NS

FAP

Accuracy 77.1429 59.2857 77.1429 77.1429 66.4236 56.4286 66.4236 66.4236

ROC

Area

0.771 0.593 0.771 0.771 0.664 0.564 0.664 0.664

FBP

Accuracy 81.4286 48.5714 62.8571 50 80.7143 81.4286 65.7143 50

ROC

Area

0.814 0.486 0.629 0.5 0.807 0.814 0.657 0.5

FW

Accuracy 85 85 78.5714 47.1429 82.1429 75.7143 78.5714 42.8571

ROC

Area

0.85 0.85 0.786 0.471 0.821 0.757 0.786 0.429

FRMS

Accuracy 75.7143 65 75.7143 75.7143 82.1429 51.4286 81.4286 82.1429

ROC

Area

0.757 0.65 0.757 0.757 0.821 0.514 0.814 0.821

FS

Accuracy 72.8571 63.5714 72.8571 72.8571 80 50.7143 78.5714 77.1429

ROC

Area

0.729 0.636 0.729 0.729 0.8 0.507 0.786 0.771

FALL

Accuracy 80 78.5714 77.1429 50.7143 80.7143 81.4286 75 50

ROC

Area

0.8 0.786 0.771 0.507 0.807 0.814 0.75 0.5

Empirical Results : for MLP

FAP FBP FW FRMS FS FALL

Accuracy 77.1429 81.4286 83.5714 80 77.1429 85.7143

ROC

Area

0.854 0.874 0.917 0.899 0.836 0.9

Empirical Results : Best 3 Classifiers

Best Mean ROC Area Chart for Best Cases. Weighted Average False-Positive Rate Chart for Best Cases.

Conclusions Wavelet-based energy-entropy is very informative feature set

among all other feature sets considered in this paper.

MLP classifier is more suitable in comparison to SVM for EEG-based motor imagery left/right hand movement while considering large numbers of features.

If we consider ROC area as a performance measure then Average power feature set performs better in comparison to Statistical feature set, it suggests that the model is a better predictor.

References 1. Information about Hans Berger : http://www.ncbi.nlm.nih.gov/pubmed/16334737

2. Graz Dataset link : http://bbci.de/competition/ii/#datasets

3. M. Murugappan, M. Rizon, R. Nagarajan, S. Yaacob, I. Zunaidi, andD. Hazry, “EEG

feature extraction for classifying emotions using FCMand FKM,” J. Comput.

Commun., vol. 1, pp. 21–25, 2007.

4. Panagiotis C. Petrantonakis and Leontios J. Hadjileontiadis, “Emotion Recognition

From EEG Using Higher Order Crossings”, IEEE TRANSACTIONS ON

INFORMATION TECHNOLOGY IN BIOMEDICINE, Vol. 14, No. 2, March 2010.

5. Saugata Bhattacharya, Anwesha Khasnobish, Somsirsa Chatterjee, Amit Konar and

D.N. Tibarewala, “Performance Analysis of LDA, QDA and KNN Algorithms in

Left-Right Limb Movement Classification from EEG Data,” International

conference on Systems in Medicine and Biology, December 2010.