a comparison of eeg processing methods to improve the ...using a population of five disable- and...

A Comparison of EEG Processing Methods to

Improve the Performance of BCI

Arjon Turnip, Demi Soetraprawata, and Dwi E. Kusumandari Technical Implementation Unit for Instrumentation Development

Indonesian Institute of Sciences, Bandung, Indonesia

Email: {arjo001, demi001, esti001}@lipi.go.id

Abstract—Electroencephalogram (EEG) recordings provide

an important means of brain-computer communication, but

their classification accuracy is limited by unforeseeable

signal variations due to artifacts or recognizer-subject

feedback. In this paper, we propose a comparison of

processing method (i.e., NPCA, JADE, and SOBI) entailing

time-series EEG signals. Finally, the promising results

reported here (up to 94% average classification accuracy

and 36.4% improvement of maximum average transfer rate)

reflect the considerable potential of EEG for the continuous

classification of mental states.

Index Terms—brain computer interface (BCI), classification

accuracy, transfer rate, NPCA, JADE, SOBI,

electroencephalogram (EEG)

I. INTRODUCTION

Many people with severe motor disabilities require

alternative methods for communication and control.

Numerous studies over the past two decades show that

scalp-recorded electroencephalography (EEG) activity

can be the basis for non-muscular communication and

control systems. With production of advanced bio-

instruments for recording and amplifying the signals as

well as cheap and powerful personal computers, this

dream was realized and Brain-Computer Interface (BCI)

was developed [1], [2]. Brain activity can be measured

using EEG. By extracting specific components from

human brain activity and linking this brain activity to

specifically developed algorithms, an interface between a

computer and the users’ brain is created. Signals from the

brain are processed to extract specific features that reflect

the user’s intentions. Today there exist various techniques

by which to accomplish this [3]-[7]. The user’s brain is

now coupled to a computer or external device, which

allow communication or controlling devices directly,

without implementing any motor action.

There are various properties in EEG that can be used

as a bases for BCI such as rhythmic brain activity (i.e.,

delta, theta, alpha, and beta) [8], event-related potentials

(ERPs), event-related de-synchronization (ERD) and

event-related synchronization (ERS) [9]. In the present

Manuscript received February 20, 2013; revised March 18, 2013;

accepted April 1, 2013. This research was supported by Indonesian

Institute of Sciences, Indonesia.

study, we focused on the use of the ERP-P300 properties.

The P300 represents the unpredictable stimuli presented

in an oddball paradigm, in which low-probability targets

are mixed with high-probability ones. For this paradigm,

the subject is told to respond to a rare stimulus that occurs

randomly and infrequently among other, frequent stimuli

[8]. The presence, magnitude, topography, and time of

the response signal are often used as metrics of cognitive

function in decision making processes. In this paper, a

comparison of extraction method (i.e., NPCA, JADE, and

SOBI) entailing time-series EEG signals is proposed. In

order to examine the performance (i.e., accuracy and

transfer rate) improvements of the proposed method, a

classification using back-propagation neural networks

(BPNN) which has been well developed in the field of

speech recognition is applied.

II. METHODS

The data set used in this study was obtained from the

website of the EPFL BCI group [8]. The data have been

recorded according to the 10-20 international standard

from the 32 electrode configurations [9]. Each recorded

signal has a length of 820 samples with a sampling rate of

2048 Hz (the EEG was down-sampled from 2048 Hz to

32 Hz by selecting each 64th sample from the band pass-

filtered data). A six-choice signal paradigm was tested

using a population of five disable- and four able-bodied

subjects. The subjects were asked to count silently the

number of times a prescribed image flashed on a screen.

Four seconds after a warning tone, six different images (a

television, a telephone, a lamp, a door, a window, and a

radio) were flashed in a random order [8]. Each flash of

an image lasted for 100 ms, and for the following 300 ms

no image was flashed (i.e., the inter-stimulus interval was

400 ms). Each subject completed four recording sessions.

Each of the sessions consisted of six runs with one run for

each of the six images. Our goal is to discriminate all

possible combinations of the pairs of mental tasks from

each other using the corresponding EEG signals.

It is difficult to compare the performances of the BCI

systems, because the pertinent studies present the results

in different ways. However, in the present study, the

comparison was made based on the accuracy and the

transfer rate. The speed of a particular BCI is affected by

the trial length, that is, the time needed for one selection.

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International Journal of Signal Processing Systems Vol. 1, No. 1 June 2013

©2013 Engineering and Technology Publishingdoi: 10.12720/ijsps.1.1.63-67

This time should be shortened in order to enhance a

BCI’s effectiveness in communication. The transfer rate

depends on both the speed and the accuracy of selection

and expressed as. If a trial has N possible selections and

each selection has the same probability of being the

desired selection, and if P denotes the probability that the

desired choice is actually selected, then the probability

for the remaining (undesired) selections being selected

will be (1-P)/(N-1). The bit rate (bits/trial) of each

selection can then be expressed as [10]

1

1log)1()(log)(log 222

N

PPPPNb (1)

where N is a number of possible selections of the target

and P denotes the probability that the desired choice is

actually selected. The transfer rate (bits per minute) is

equal to b multiplied by the average speed of selection S

(trial per minute, which is equal to the reciprocal of the

average time required for one selection). Therefore, based

on the data sets information, the desired output signal is

developed.

III. EXTRACTIONS AND CLASSIFICATION METHOD

A. Nonlinear Principle Component Analysis

Let x(k) represent n-dimensional vectors which

correspond to the n continuous time series from the n

EEG channels. Then xi(k) corresponds to the continuous

sensor readings from the ith EEG channel. Because

various underlying sources are summed via volume

conduction to give rise to the scalp EEG, each of the xi(k)

are assumed to be an instantaneous linear mixture of n

unknown components or sources si(k), via the unknown

mixing matrix A [7]

)()()( knkAskx (2)

where TM kxkxkxkx )](,),(),([)( 21 MR is a noisy

sensor vector of EEG signals, NMA R with entries ija

is an unknown NM x mixing matrix,

TN ksksksks )](,),(),([)( 21

NR is an unknown

source vector signals, and MT

M knknkn R)](,),([)( 1 is a vector of additive

noises. The objective of this work is to design a feed-

forward neural network and an associated adaptive

learning algorithm that enable estimation of the source

s(k) and identification of the mixing matrix A and

separating matrix W with a good tracking abilities for

time variable systems. These objective can be achieve

using the flowchart in the Fig. 1.

B. Joint Approximate Diagonalization of Eigen Matrices

Joint approximate diagonalization of eigen matrices

(JADE) is based on diagonalization of cumulant matrices.

In the case of JADE the matrix W diagonalizes F(M) for

any i.e., WF(M)WT is diagonal. Matrix F is a linear

combination of terms of the form Tiiww , w is a column of

W. Ti WMWF )( is made as diagonal as possible for

different combination of iM and ki ,,1 . The

diagonality of matrix Ti WMWFQ )( can be measured

as the sum of the squares of off-diagonal elements:

Observe n-dimentional data

vector x according to: x=As

Determining the pre-separating matrix (V)

Applying the learning rule:

Determine pre-separating vector ( ) by:

)}()({ kxkxET

i

x

)()()( kxkVkx

)]()()()[()()()1( kxkVkxkxkkVkV

Determining whitening matrix (P)


Determine the whitening vector (u) by:)()()( kxkPku

)()]()()[()()1( kPkukuIkkPkPT

n

2

2

E{P(k)PT(k)}=1

Yes

NoYes

No

Separate the independent components

Determining separating matrix (W)


)(])()([)()()()1( kWkyfkukyfkkWkW TT

Is decorrelated with)(kx

Is W converged to a

desired value?

Yes

No

Separate independent componets

according to: y(k)=W(k)u(k)

Estimate the mixing matrix (A)

Calculate the estimating matrix (Q)


)()]()()(ˆ)[()()1( kykykQkxkkQkQT

Observed the observed signals by:

)()()(ˆ kykQkx

Determine the correlation

coefficient of signals

Is the value

acdepted?

End

2

1

1

3

3

Figure 1. Nonlinear principal component cnalysis flowchart.

lk klq2

. Minimization of sum of squares of diagonal

elements is same as maximization of sum of squares of

diagonal elements [10], [11].

2)()( iT

iJADE WMWFdiagWJ (3)

Joint approximate diagonalization of )( iMF can be

obtained by maximizing JADEJ . Choice of the matrix

iM would be to take the eigen matrices of the cumulant.

After algebraic manipulations, the above equation

becomes

2),,,()( iiklijkl lkjiJADE yyyycumWJ (4)

when the above equation is minimized the sum of squares

of cross-cumulants of iy is also minimized.

C. Second-Order Blind Identification

The Second-Order Blind Identification (SOBI) is

applied as a blind source separation to EEG data collected

based visual stimulation with the goal of performing

classification of event-related potentials (ERPs) elicited

under different stimulation condition. SOBI uses the EEG

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©2013 Engineering and Technology Publishing

measurements x(k) and nothing else to generate an

unmixing matrix W that approximates A-1

in the Eq. 2,

and the vector of the estimated component values y(k) in

the Fig. 1. Sensor space projections, which indicate the

effect of a given component on all sensors are given by

the estimated mixing matrix 1ˆ WA . The SOBI

algorithm proceeds in two stages: First, the sensor signals

are zero-meaned and presphered as follows [12]

)()()( kxkxBky (5)

The angle brackets denote an average over time, so

the subtraction guarantees that y will have a mean of zero.

The matrix B is chosen so that the correlation matrix of

Tkykyy )()(, , becomes the identity matrix. This is

accomplished by moving to the PCA basis using

Ti UB 2/1diag (6)

where i are the eigenvalues of the correlation matrix,

Tkxkxkxkx )()()()( (7)

and U is the matrix whose columns are the corresponding

eigenvalues, that is, the PCA components of x. The

second stage, one constructs a set of matrices that, in the

correct separated basis, should be diagonal. A set of time

delay values s is chosen to compute symmetrized

correlation matrices between the signal y(k) and a

temporally shifted version:

TkykyR )()(sym (8)

where 2/)()(symTMMM is a function that takes

an asymetric matrix and returns a closely related

symmetric one. This symetrization discards some

information, but the problem is already highly valid,

albeit slightly weaker, constraints on the solution. The

rotation of V that jointly diagonalizes all of them is

calculated by minimazing 2

ijjiTV VR , the sum

of the squares of the off-diagonal entries of the matrix

products VRTV through an iterative process. The final

estimate of the separation matrix is

BTVW (9)

which is used to derive the separated component of y(k).

To measure the performance of algorithms, we use the

performance index (PI) as in [11], [12] defined by

n

i

n

k jij

kin

k ijj

ik

g

g

g

g

nnPI

1 11

1max

1max1

1 (10)

where G is the global transformation matrix from s to y,

gij is the (i,j) -element of the global system matrix G=HW

and maxj gij represents the maximum value among the

elements in the ith row vector of G, maxj gji does the

maximum value among the elements in the ith column

vector of G. When the perfect separation is achieved, the

performance index is zero. In practice, the values of

performance index around 10-2

gives quite a good

performance.

D. Backpropagation Neural Networks Classifier

Neural networks have been proposed in the fields of

neural sciences following research into the mechanisms

and structures of the brain. The back-propagation

algorithm allows exponential acquisition of input-output

mapping knowledge within multilayer networks. If a

pattern is submitted and its classification is determined to

be erroneous, the current least mean-square classification

error is reduced. The error is expressed as [13]

n

i

ijijjT

ijijjj wxydwxydE1

)),(()),((2

1 (11)

where jd denotes the desired output of node j

corresponding to input ix , n is the number of training

patterns and ),( ijij wxy denotes the vector output of the

networks corresponding to input ix and weight matrix

ijwW . During the association or classification phase, the trained neural network itself operates in a feed-

forward manner. The error is therefore a function of the

weights of the input and output layers. The back-

propagation algorithm is a gradient descent method

minimizing the mean square error between the actual and

target outputs of a multilayer perceptron. Using the

sigmoid nonlinearity

neti enetf

1

1)( (12)

the back-propagation algorithm consists of the following

steps: First, initialize all weights and node offsets to small

random values. Second, present continuous input vector

ix and specify desired output jd . The output vector

elements are set to zero values except for that

corresponding to the class of the current input. Third,

calculate the actual output vector y using the sigmoid

nonlinearity. Fourth, adjust the weights by

ijijij xtwtw )()1( (13)

where j is the sensitivity of node j. Fifth, repeat the

steps from the second step. A better approach is a cross-

validation technique, which stops training when the error

on a separate validation set reaches a minimum. We

observe records a vectors of EEG signals )(tx

Tm txtxtx )](,),(),([ 21 from a multiple-input/ multiple-

output nonlinear dynamical system. The objective is to

find an inverse system, termed a reconstruction system

with back-propagation neural networks (BPNN), in order

to estimate the primary input source of brain signals

65



Tn tstststs )](,),(),([)( 21 corresponding to particular

stimulus, which are represented by )(ty

Tn tytyty )](,),(),([ 21 .

IV. RESULTS AND DISCUSSION

In brain research, the ability to measure single-trial

ERPs is one important step toward the understanding of

how the relative timing of neuronal activity can affect

learning and how memory of a particular experience can

be encoded rapidly with a single or very few exposures.

In the present study, a BPNN classifier was used. In order

to cope with nonlinearly separable problems, additional

layers of neurons placed between the input layer and the

output neuron are needed, leading to the multilayer

perceptron architecture. Performance is measured

according to the specified performance function such as

iteration speed and signal noise to ratio (SNR) criteria

[14]. The robustness of the each algorithm (NPCA, JADE,

and SOBI) was evaluated by comparing its separation

performance as shown in Fig. 2. After 500 iteration, the

NPCA algorithm perform slightly better performance and

reach around 10-2

PI after 2500 iteration. This values

indicate that NPCA give shortest extraction time compare

to others algorithm. Fig. 3 shows typical performances of

the three algorithms discussed in this paper. At high SNR,

all tested algorithms perform very well. At low SNR, one

can observe that the NPCA gives better performance than

the other algorithms in most SNR ranges. In 0 - 4dB

range JADE is worse than the others.

0 500 1000 1500 2000 2500 3000 3500 40000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Number of iterations k

PI(

k)

NPCA

JADE

SOBI

Figure 2. Evolutions of PI(k) of the NPCA, JADE, and SOBI algorithms.

The data sets for subject 5 were not included in the

simulation since the subject misunderstood the

instructions given before the experiment. Comparative

plots of the classification accuracies and transfer rates

(obtained with the BPNN classifier method and averaged

over eight electrode configurations) for the disable- (S1 -

S4) and able-bodied subjects (S6 - S9) are depicted in Fig.

4 and Fig. 5, respectively. All of the subjects, except for

subjects 6 and 9, achieved an average classification

accuracy of 100% after ten blocks of stimulus

presentations were averaged (i.e., 24.5 s). The reason for

the poorer performance of subject 9 might be fatigue.

Subject 6 reported that he accidentally concentrated on

the wrong stimulus during one run in session 1 [8].

Shown alongside the classification accuracies using

BPNN for all of the subjects, in Table I, are the

corresponding 92% confidence intervals which the

classified based NPCA extraction gives the best

accuracies and transfer rates with smallest standard

deviations

-4 0 4 8 12 16 20 24 28 32 36-10

2

-101

-100

SNR [dB]P

erf

orm

ance I

ndex

NPCA

JADE

SOBI

Figure 3. Comparison of performance index of the NPCA, JADE, and SOBI algorithms as a function of signal to noise ratio (SNR).

0 5 10 15 20 25 30 35 40 45 500

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%)

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Figure 4. Comparison of classification accuracy and transfer rate plots (averaged over eight electrode configurations) obtained with

BPNN classifier for disabled subjects.

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Figure 5. Comparison of classification accuracy and transfer rate plots (averaged over eight electrode configurations) obtained with

BPNN classifier for able-bodied subjects.

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TABLE I. AVERAGE CLASSIFICATION ACCURACY (%).

Subject SOBI JADE NPCA

S1 94.00 93.00 95.75

S2 92.25 94.5 94.00

S3 94.00 95.5 95.00

S4 93.00 94.25 94.50

S6 90.50 91.85 92.55

S7 94.75 94.00 96.20

S8 94.75 96.00 97.75

S9 93.70 92.95 94.00

Average (S1–S4) 93.30.8 94.31.0 94.50.7

Average (S6-S9) 93.42.0 93.71.7 94.00.5

Average (all) 93.41.4 94.01.3 94.30.6

V. CONCLUSION

The results presented in this study show that compared

with the JADE and SOBI algorithms, a better extraction

result can be obtained when using the NPCA algorithm

for single-trial ERPs based on the P300 component from

specific brain regions. With NPCA extraction, the data

indicate that a P300-based BCI system can communicate

at the rate around 25.4 bits/min and 36.5 bits/min for the

disable- and able-bodied subjects, respectively. The

average of 100% classification accuracy is achieved after

nine blocks (average) for disabled subjects and after

fourteen blocks (average) for able-bodied subjects. These

results indicate that the system allowed several disabled

users to achieve transfer rates significantly beyond those

reported previously in the literature.

ACKNOWLEDGMENT

This research was supported by the tematic program

(No. 3425.001.013) through the Bandung Technical

Management Unit for Instrumentation Development

(Deputy for Scientific Services) and the competitive

program (No. 079.01.06.044) through the Research

Center for Metalurgy (Deputy for Earth Sciences) funded

by Indonesian Institute of Sciences, Indonesia.

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[13] Y. Yang, S. S. Ge, T. H. Lee, and C. Wang, “Facial expression recognition and tracking for intelligent human-robot interaction,”

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Arjon Turnip received the B.Eng. and M.Eng. degrees

in Engineering Physics from the Institute of Technology Bandung (ITB), Indonesia, in 1998 and 2003,

respectively, and the Ph.D. degree in Mechanical Engineering from Pusan National University, Busan,

Korea, under the World Class University program in

2012. He is currently work in the Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences,

Indonesia as a research coordinator. He received Student Travel Grand Award for the best paper from ICROS-SICE International Joint

Conference 2009, Certificate of commendation: Superior performance

in research and active participation for BK21 program from Korean government 2010, and JMST Contribution Award for most citations of

JMST papers 2011. His research areas are integrated vehicle control, adaptive control, nonlinear systems theory, estimation theory, signal

processing, brain engineering, and brain-computer interface.

Demi Soetraprawata received the Bachelor degree in

Engineering Physics from National University of Jakarta (UNAS) and the Master degree in Instrumentation and

Control from Institute of Technology Bandung (ITB),

Indonesia. He is currently work in the Technical Implementation Unit for Instrumentation Development

(UPT BPI), Indonesian Institute of Sciences (LIPI), Indonesia as a Chairman. Several activities that he had attended related to the

instrumentation are about analytical instrumentation at Queensland

University of Technology (QUT) and medical instrumentation at the Royal Brisbane Hospital, in 1990 and 1992, respectively, Brisbane,

Australia. His research areas are control engineering, instrument technology, engineering measurements, and instrumentation for

calibration and metrology.

Dwi Esti Kusumandari received the B.Eng. degrees in Engineering Physics from the Institute of Technology

Bandung (ITB), Indonesia, in 1999, and the M.Eng.

degree in Biomedical Engineering from the same institute in 2006. She is currently work in the Technical

Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Indonesia as a junior

researcher. Her research interest are in biomedical engineering, image

processing, signal processing, and instrumentation.

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