Performance comparison of neural network training methods based on wavelet packet transform for classification of five mental tasks

doi:10.4236/jbise.2010.36083

Paper Menu >>

Journal Menu >>

J. Biomedical Science and Engineering, 2010, 3, 612-617

doi:10.4236/jbise.2010.36083 Published Online June 2010 (http://www.SciRP.org/journal/jbise/

JBiSE

Published Online June 2010 in SciRes. http://www.scirp.org/journal/jbise

Performance comparison of neural network training methods

based on wavelet packet transform for classification of five

mental tasks

Vijay Khare1, Jayashree Santhosh2, Sneh Anand3, Manvir Bhatia4

1Jaypee Institute of Information Technology, Department of Electronics and communication Engineering, New Delhi, India;

2Indian Institute of Technology, Computer Services Centre, New Delhi, India;

3Indian Institutes of Technology, Centre for Biomedical Engineering, New Delhi, India;

4Department of Sleep Medicine, New Delhi, India.

Email: [email protected]; [email protected]; [email protected]rnet.in; [email protected]

Received 11 February 2010; revised 15March 2010; accepted 25 March 2010.

ABSTRACT

In this study, performances comparison to discrimi-

nate five mental states of five artificial neural net-

work (ANN) training methods were investigated.

Wavelet Packet Transform (WPT) was used for fea-

ture extraction of the relevant frequency bands from

raw electroencephalogram (EEG) signals. The five

ANN training methods used were (a) Gradient De-

scent Back Propagation (b) Levenberg-Marquardt (c)

Resilient Back Propagation (d) Conjugate Learning

Gradient Back Propagation and (e) Gradient Descent

Back Propagation with movementum.

Keywords: Electroencephalogram (EEG); Wavelet

Packet Transform (WPT); Artificial Neural Network

(ANN)

1. INTRODUCTION

Brain computer interface use a non muscular communi-

cation channel for conveying message and command to

the external words in the absence of biological channels

[1-3]. Neuromuscular disorders like Amyotrophic lateral

sclerosis can temporarily or permanently impair spoken

and physical communication. Those most severely af-

fected may lose all voluntary muscle control and may be

completely locked in to their bodies, unable to commu-

nicate in any way. Using cognitive abilities is sometimes

the only the way to restore communication and motor

function [4-7]. Through training, subjects can learn to

control their brain activity in predefined fashion that is

classified by pattern recognition algorithms. Accuracy of

classification is affected by the quality of EEG signals

and the processing algorithms. The processing algo-

rithms include preprocessing, feature extraction and

classification. Previous studies investigated the effect of

different feature extraction algorithms with along and

the different mental tasks on classification accuracy was

investigated [8,9].

In this study, wavelet packet transform (WPT) method

was used to capture the information of mental tasks from

eight channel EEG signals of nine subjects. The coeffi-

cients of wavelet packet transform (WPT) were used as

the best fitting input vector for ANN. Five various artifi-

cial neural networks (ANN) training methods were used

to compare the performance in discrimination of five

mental tasks.

2. METHODOLOGY

2.1. Subjects

Nine right-handed healthy male subjects of age (mean

23yr) having no sign of any motor- neuron diseases were

selected for the study. A pro-forma was filled in with

detail of their age & education level. The participants

were student volunteers for their availability and interest

in the study. EEG data was collected after taking written

consent for participation. Full explanation of the ex-

periment was provided to each of the participants.

2.2. EEG Data Acquisition

EEG Data used in this study was recorded on a Grass

Telefactor EEG Twin3 Machine available at Deptt. of

Neurology, Sir Ganga Ram Hospital, New Delhi. EEG

recording was done for five mental tasks for five days,

from nine selected subjects. Data was recorded for 10

sec during each task and each task was repeated five

times per session per day. Bipolar and Referential EEG

was recorded using eight standard positions C3, C4, P3,

P4, O1 O2, and F3, F4 by placing gold electrodes on

scalp, as per the international 10-20 standard system of

electrode placement as shown in Figure 1. The settings

V. Khare et al. / J. Biomedical Science and Engineering 3 (2010) 612-617 613

Figure 1. Montage for present study.

Relax Mental

Task

Sec

…

Figure 2. Timing of the Protocol.

used for data collection were: low pass filter 1Hz, high

pass filter 35 Hz, sensitivity 150 micro volts/mm and

sampling frequency fixed at 400 Hz. The reference elec-

trodes were placed on ear lobes and ground electrode on

the forehead. EOG (Electooculargram) being a noise

artifact, was derived from two electrodes placed on outer

canthus of left and right eye in order to detect and elimi-

nate eye movement artifacts.

2.3. Experiment Paradigm

An experiment paradigm was designed for the study and

the protocol was explained to each participant before the

experiment. In this, the subject was asked to comfortably

lie down in a relaxed position with eyes closed. After

assuring the normal relaxed state by checking the status

of alpha waves, the EEG was recorded for 50 sec, col-

lecting five session of 10 sec epoch for the relaxed state.

This was used as the baseline reference for further analy-

sis of mental task. The subject was asked to perform a

mental task on presentation of an audio cue. Five session

of 10 sec epoch for each mental task were recorded as

shown in Figure 2. The whole experimental lasted for

about one hour including electrodes placement.

Data collected from nine subjects performing ﬁve

mental tasks were analyzed. The following mental tasks

were used:

Relaxed: The subject was asked to relax with their

eyes closed. No mental or physical task to be performed

at this stage.

Arithmetic Task: The subject was asked to perform

simple arithmetic (SA) and complex arithmetic (CA). An

example of a trivial calculation is to multiply 2 by 3 and

nontrivial task is to multiply 49 by 78. The subject was

instructed not to vocalize or make movements while

solving the problem. EEG signal were recorded corre-

sponding.

Geometric Figure Rotation (R): The subject was given

30 seconds to see complex three dimensional objects,

after which the object was removed. The subject was

instructed to visualize the object being rotated about an

axis. The EEG signals were recorded during this period.

Movement Imagery (M): The subject was asked to

plan movement of the right hand and corresponding

EEG signals were recorded during this period.

2.4. Feature Extraction

The frequency spectrum of the signal was first analyzed

through Fast Fourier Transform (FFT) method [10]. The

FFT plot of signals from the all electrode pairs were ob-

served and maximum average change in EEG amplitude

was noted as shown in Figures 3-7.

For relaxed, the peaks of power spectrum almost co-

incide for central area in the alpha frequency range (8-13

Hz)[11]. EEG recorded with relaxed state is considered

to be the base line for the subsequent analysis. Mu

rhythms are generated over sensorimotor cortex during

planning a movement. For movement imagery (M) of

right hand, maximum up to 50% band power attenuation

was observed in contralateral (C3 w.r.t C4) hemisphere

in the alpha frequency range (8-13 Hz) [11,12]. For geo-

metrical figure rotation(R), the peak of the power spec-

trum was increased in right hemisphere rather than left

in the occipital area for the alpha frequency range (8-13

Hz) [13]. For simple arithmetic(SA), the peak of the

power spectrum was increased in left hemisphere rather

than right hemisphere in the frontal area for the alpha

frequency range (8-13 Hz) [14].For complex arithmetic

(CA), the peak of the power spectrum was increased in

left hemisphere rather than right hemisphere in the parie-

tal area for the alpha frequency range (8-13 Hz).

100

150

200

C3 C4

Ele ct r ode

Power Spectrum Density for Alpha Frequency

Range

Figure 3. Power Spectra for Relax state at C3 and C4 channel.

614 V. Khare et al. / J. Biomedical Science and Engineering 3 (2010) 612-617

100

150

Electrode

Power Spectrum Density for Alpha Frequency

Range

Figure 4. Power Spectra for simple arithmetic at F3 and F4 channel.

100

150

200

O2 O1

Electr ode

Power Spectrum Density for Alpha Frequency

Range

Figure 5.Power Spectra for rotation at O1 and O2 channel.

100

150

200

250

300

Electrode

Range

Power Spectrum Density for Alpha Frequency

Figure 6. Power Spectra for complex Arithmetic P3 and P4 channel.

100

150

200

C3 C4

El e ctr od e

Power Spectrum Density for Alpha Frequency

Range

Figure 7. Power Spectra for right movement at C3 and C4 channel.

2.5. Wavelet Packet Transform

By applying Wavelet packet analysis on the original

signal wavelet coefficients in the 8-13 HZ frequency

band at the 5th level node (5, 3) were obtained. The

signal was reconstructed at node (5, 3). These coeffi-

cients are scaled and WPT coefficients are used as the

best fitting input vector for ANN. Wavelet transform

we were able to reduce 1 second of EEG data to 21

coefficients for each mental tasks [15,16].

2.6. Classification

The main advantage of choosing artificial neural net-

work for classification was due to fact that ANN’s could

be used to solve problems, where description for the data

is not computable. ANN could be trained using data to

discriminate the feature. The five different training

methods used for Classification in the present study were

Gradient Descent method Resilient Back propagation,

Levenberg-Marquardt, Conjugate Gradient Descent and

Gradient Descent back propagation with movementum.

For classification a two layer neural networks was

used for the instance a topology of {10, 1} indicate a 21

input, 10 neurons in hidden layer and one output layer.

The neural network was designed to accept a 21 element

input vector and give a single output. One second seg-

ments of EEG were classified. The training set was

composed of 60% EEG trial per mental tasks and test set

was composed 40% EEG trial per mental task. The out-

put was designed to give 0 for baseline and 1 for task.

The neural network was trained for a fixed number of

epochs and the training is done using a five learning

techniques [17]. Parameter used for five training meth-

ods of neural network for classification of five mental

tasks as shown in the Table 1.

2.7. Evaluations

Performance (RC) is defined as ratio between correctly

classified patterns in the test set to the total number of

patterns in the test set in percentage.

Number of correctly classified test patterns

Rc Total number of patterns in the test set



With the help of above formula we calculate the per-

formance of each method for each task [18].

3. RESULTS

For the classification of five mental tasks neural network

training methods were used. Nine male right-handed

subjects participated in the experiments. The subjects

asked to perform five mental tasks namely relaxed, arith-

metic task (simple and complex multiplication), geomet-

rical figure rotation, and movement imagery. Table 2

showed the comparison of the performance of five neu-

V. Khare et al. / J. Biomedical Science and Engineering 3 (2010) 612-617 615

Table1. Parameter used for different back propagation algo-

rithms with topology {10, 1}.

Gradient Descent with Momentum (GDM)

Topology {10,1} α = 0.01. Mu = 0.01

MSE = 1e-5

Epoach = 5000

Gradient Descent method (GD)

Topology {10,1} α = 0.01

MSE = 1exp-(5)

Epoach = 5000

Resilient Back propagation (RBP)

Topology {10,1} α = 0.01

MSE = 1exp-(5)

Epoach = 5000 β = 0.75 and β1 = 1.05

Conjugate Gradient descent (CG)

Topology {10,1} α = 0.01

MSE = 1exp-(5)

Epoach = 5000

Levenberg-Marquardt (LM)

Topology {10,1} Mu = 0.01

MSE = 1exp-(5)

Epoach = 5000 Mu_dec = 0.1 and Mu_inc = 10

ral network (NN) training methods in classifying of five

mental tasks. From this table we can say that Resilient

Back Propagation method was most suitable for the clas-

sification of all five mental tasks. Because this method

gave highest performance (95%) all five mental tasks.

Percentage average accuracy shown in the Figures 8-

12.

4. CONCLUSIONS

In this study, nine healthy male subjects were se-

lected to investigate MLP classifier with various training

methods to discriminate five mental tasks (relaxed state,

movement imagery of right hand, geometrical figure

rotation, arithmetic simple task, and arithmetic complex

task) effectively. For relaxed, the peaks of power spec-

Gradient Descent back Propagation

100

SA CARM

mental tasks

pecentage of

accuracy

Figure 8. Classification accuracy using GDA BP train-

ing methods.

trum almost coincide in central area at a particular based

frequency. For simple arithmetic, it was observed that

the amplitude of the power spectrum for alpha frequency

range (8-13 Hz) increased left hemisphere rather than

right hemisphere in frontal region. For complex arithe-

matic, it was observed that the amplitude of the power

spectrum for alpha frequency range (8-13 Hz) increased

Resilient Back propagation

SA CARM

Mental tasks

percentage of

accuracy

Figure 9. Classification accuracy RBP training methods.

Conjugate Gradient BP

SA CARM

Mental tasks

percentage of

accu racy

Figure 10. Classification accuracy CGBP training methods.

Gradient descent method with

movementum

SA CARM

Mental tasks

percentage of

accuracy

Figure 11. Classification accuracy GDM training methods.

Levenberg-Marquardt

SA CARM

Mental tasks

percenatge of

accu racy

Figure 12. Classification accuracy LM training methods.

616 V. Khare et al. / J. Biomedical Science and Engineering 3 (2010) 612-617

Table 2. Comparisons of Different NN training Methods.

Tasks Baseline and

Arithmetic simple

Baseline and Arithmetic

complex Baseline and Rotation Baseline and movement

Techniques Correct

classification

Wrong

classifica-

tion

Correct

classifica-

tion

Wrong

classifica-

tion

Correct

classifica-

tion

Wrong

classifi-

cation

Correct

classifica-

tion

Wrong

classifica-

tion

Gradient Descent Back

Propagation 95% 5% 95% 5% 87.5% 12.5% 90% 10%

Resilient Back Propagation 97.5% 2. 5% 95% 5% 95% 5% 95% 5%

Conjugated Gradient BP 97.5% 2.5% 92.5% 7.5% 92.5% 7.5% 92.5% 7.5%

GD BP with Momentum 95% 5% 95% 5% 90% 10% 90% 10%

Levenberg-Marquardt 95% 5% 90% 10% 90% 10% 92.5% 7.5%

left hemisphere rather than right hemisphere in parietal

region. For geometrical figure rotation, the peak of the

power spectrum in the alpha frequency range (8-13 Hz)

increased right occipital area. For movement imagery,

the peak of the power spectrum in the alpha frequency

range (8-13 Hz) had an attenuation central area. The

result showed the performance of neural network with

various training method, for classifying of mental tasks

w.r.t baseline. Resilient backpropagation training method

has best performance among all the training methods for

classification of mental tasks w.r.t baseline.

JBiSE

The authors would like to extend the work with se-

verely disabled people and to customize the device as

per individual response and requirements. This kind of

system can also be used in a variety of applications like–

Environment control units (ECU’S), helping disable

people to directly interact with hand held devices such as

cell phones and PDAs.

5. ACKNOWLEDGEMENTS

The authors would like to acknowledge their gratitude to the Centre of

Biomedical Engineering of IIT New Delhi and Electronics and com-

munication department of JIIT Noida. The authors also thank the sci-

entific and technical staff of EEG Laboratory of Sir Ganga Ram hospi-

tal, New Delhi for the help in carrying out the experiment.

REFERENCES

[1] Wolpaw, J.R., Birbaumer, N., Mc Farland, D.J., Plurtscheller,

G. and Vaughan, T.M. (2002) Brain computer interfaces for

communication and control. Clinical Neurophysiology,

113(6), 767-791.

[2] Pfurtschelle, G., Flotzinger, D. and Kalcher, J. (1993)

Brain computer interface－A new communication device

for handicapped people. Journal of Microcomputer Ap-

plications, 16, 293-299.

[3] Wolpaw, J.R., Vaughan, T.M. and Donchin, E. (1996)

EEG based communication prospects and problems.

IEEE Transactions on Rehabilitation Engineering, 4,

425-430.

[4] Lotte, F., Congedo, M., Lecuyer, A., Lamarche, F. and

Arnaldi, B. (2007) A review of classification algorithms

for EEG bases brain computer interface. Journal of

neural Engineering, 4, 1-13.

[5] Wolpaw, J.R., Leob, G.E., Allison, B.Z., Donchin, E. and

Turner, J.N. (2006) BCI Meeting 2005－Wokshop on

signals and rerecording methods. IEEE Transactions on

Neural Systems and Rehabilitation Engineering, 14, 138-

141.

[6] Anderson, C.W., Stolz, E.A. and Shamsunder, S. (1998)

Multivariable autoregressive model for classification of

spontaneous electroencephalogram during mental tasks.

IEEE Transactions on Biomedical Engineering, 45, 277-

278.

[7] Boostani, R., Graimann, B., Moradi, M.H. and Plurf-

scheller, G. (2007) Comparison approach toward finding

the best feature and classifier in cue BCI. Journal of

Medical and Biological Engineering and Computing, 45,

403-413.

[8] Palaniappan, R. (2006) Utilizing gamma band to improve

mental task based brain－Computer interface design.

IEEE Transactions on Neural and Rehabilitation Systems

Engineering, 14, 299-303.

[9] Keirn, Z.A. and Aunon, J.I. (1990) A new mode of

communication between man and his Surroundings. IEEE

Transactions on Biomedical Engineering, 37, 1209-1214.

[10] Palaniappan, R. (2005) Brain computer interface design

using band powers extracted during mental task. Procee-

ding of the 2nd International IEEE EMBS Conference on

Neural Engineering, Arlington, 321-324.

[11] Pfurtscheller, G., Neuper, C., Schlogl, A. and Lugger, K.

(1998) Separability of EEG signals recorded during right

and left motor imagery using adaptive auto regressive

parameters. IEEE Transactions on rehabilitation Engin-

eering, 6, 316-325.

[12] Santhosh, J., Bhatia, M., Sahu, S. and Anand, S. (2004)

Quantitative EEG analysis for assessment to plan a task

in ALS patients, a study of executive function (planning)

in ALS. Journal of Cognitive Brain Research, 22, 59-66.

[13] Nikolaev, A.R. and Anokhin, A.P. (1998) EEG frequency

ranges during reception and mental rotation of two and

three dimensional objects. Neuroscience and Bheaviour

physiology, 29, 203-223.

[14] Osaka, M. (1984) Peak alpha frequency of EEG during a

mental task: Task difficulty and hemisphere difference.

Journal of Psychophysiology, 21, 101-105.

[15] Ting, W., Zhenga, Y.G., Bang-huaa, Y. and Hong, S.

(2008) EEG feature extraction based on wavelet packet

V. Khare et al. / J. Biomedical Science and Engineering 3 (2010) 612-617 617

decom-position for brain computer interface. Measurement,

Elsevier Journal, 41, 618-625.

[16] Akay, M. (1995) Wavelet in biomedical engineering.

Journal of Annals of Biomedical Engineering, 23, 529-

530.

[17] Ravi, K.V.R. and Palaniappan, R. (2006) Neural Network

Classiﬁcation of Late gamma band electroenceph-alogram

features, Soft Computing, 10, 163-169.

[18] Cheng, M., Gao, X., Gao, S. and Xu, D. (2002) Design

and implementation of a brain computer interface with

high transfer rates. IEEE Transactions on Biomedical

Engineering, 49, 1181-1186.