Analysis of Effective Channel Placement for anEEG-based Biometric System

Muhammad Kamil Abdullah#1, Khazaimatol S Subari#2, Justin Leo Cheang Loong∗3 and Nurul Nadia Ahmad#4

#Faculty of Engineering, Multimedia University, Cyberjaya, MY1kamil.abdullah@mmu.edu.my

2khazaimatol@mmu.edu.my4nurulnadia.ahmad@mmu.edu.my

∗Faculty of Engineering and Technology, Multimedia University, Melaka, MY3clleo@mmu.edu.my

Abstract— This paper discusses the potential of the EEG signalfor implementation of a practical biometric system using 4 orless channels of 2 different types of EEG recordings. Studieshave shown that the EEG signal has biometric potential becausethe signal varies from person to person and is impossible toreplicate and steal. Data were collected from 10 male subjectswhile resting with eyes open and eyes closed in 5 separate sessionsconducted over a course of 2 weeks. Features were extractedusing the autoregressive (AR) model and analyzed to obtain thefeature set. Results show that data from eyes open and eyesclosed using 4 channels gave good classification rates of 96% and97% respectively and that data recorded from 2 channels gaveclassification rates from 90% to 95%. Classification rates from 1channel ranged from 70% to 87%. The average time taken forrecognition was 0.38 seconds at the point of recognition. Based onthese results, there is potential for implementation of an EEG–based biometric system.

Keywords—Biometric, EEG, Autoregressive Model, NeuralNetworks

I. INTRODUCTION

ABIOMETRIC system uses the unique features of a humanbeing such as fingerprints, iris, speech and signature

to identify or authenticate a person. However, falsificationof these features has been reported over the years whichsuggests its vulnerability to forgery, duplication and theft.Therefore, new types of physiological features that are uniqueand cannot be replicated are proposed for such a system. Thispaper discusses the electroencephalogram (EEG) signal as abiometric.

The EEG is the summation of electrical activity of billionsof nerve–cell connections in the brain cortex [9]. It is measuredusing electrodes that are placed on several locations on thescalp. The main advantage of using EEG is its uniqueness;the electrical activities were observed to be different for eachperson and cannot be faked or duplicated [2], [6], [7], [8],[12]. Therefore, it is impossible to forge or steal.

To date, recording the EEG signal can be a tedious process;subjects have to wear an EEG cap, electrodes that are smearedwith a conductive gel are then attached to the scalp. Regardlessof a unipolar or a bipolar electrode configuration, both usesthe same number of electrodes.

A standard EEG cap allows placement of up to 61 elec-trodes. Obviously, for a practical system, less number ofchannels should be used. Since much time is spent to set upthe subject, a fast recognition time would be essential.

This study investigates the implementation of a practicalEEG–based biometric system in terms of the recognitionalgorithm and the stimulus–or lack of stimulus presented tothe subject while the recording was made. Special focus isgiven on the number of channels used: 4, 2 and 1 channels ofEEG were tested and the locations on the scalp that gave thehighest classification rates were recorded as well as the timeit took for recognition.

Many studies have reported excellent results using visualevoked potentials (VEP) (e.g., [7], [8]), but this requires thesubject to undergo several tasks which may be impractical foran identification system. Therefore, this study will view theclassification rates without the use of VEP.

II. EEG IDENTIFICATIONAND AUTHENTICATION STUDIES

A study by Paranjape et al. (2001) suggests that EEG hasbiometric potential as they were able to discriminate between40 subjects with 8 channels using autoregressive models witha correct classification rate of 82% [9]. Poulos et al. (1999)collected 1 channel of EEG from 75 subjects in one sessionand obtained a classification rate of 91% thus corroboratingthe evidence that the EEG signal carries genetically specificinformation and suitable for person identifcation [10].

Ravi and Palaniappan (2005) used a total of 61 channelsto record VEP EEG signals from 20 subjects [11]. Theywere able to authenticate subjects with the best classificationperformance of 95%. Palaniappan and Mandic (2007) alsousing VEP, were able to achieve an accuracy of 98% for40 individuals using 61 channels [8]. Another study by Sun(2008) used the signal from 15 electrodes of 9 subjectsimagining movements to a visual cue with a success rate of94% [13].

Some studies were based on EEG as an authentication tool.Riera et al. (2008) collected data from 51 subjects and 36intruders [12]. The EEG were recorded from 2 channels while

2010 IEEE EMBS Conference on Biomedical Engineering & Sciences (IECBES 2010), Kuala Lumpur, Malaysia, 30th November - 2nd December 2010.

978-1-4244-7600-8/10/$26.00 ©2010 IEEE 303

subjects were sitting with eyes closed for 1 minute. Theyobtained a true acceptance rate of 96.6% and false acceptancerate of 3.4%. Hema et al. (2008) recorded EEG signals from 3channels of 6 subjects and achieved an average authenticationrate of 97% [3] . Another study by Hu (2009) was ableto achieve accuracy ranging from 75% to 80% for subjectauthentication and 75% to 78.3% for identification using 6channels [4] . In the study, subjects were asked to imagine leftand right hand movements, and tongue and foot movements.

III. FEATURE EXTRACTION THROUGH AUTOREGRESSIVEMODEL

The EEG is a non–stationary signal and must be evaluatedusing short time segmentation of the signal [5]. EEG can alsobe viewed as being comprised of multiple peaks representingcombinations of delta, theta, alpha, beta and gamma waves.These peaks can be represented as a pole. Therefore, an all–pole auto–regressive (AR) model of the EEG can be estimatedby using the Yule-Walker method.

The AR model is able to provide superior resolution forshort data segments and able to filter white noise. thereforemaking it suitable for modeling EEG signals. The order ofthe model was determined through experiments because ifthe order is too low, the model will be unable to describeor estimate the signal, if the order is too high, the variance ofthe estimation would increase which would create false peaks.

IV. METHODOLOGY

The following subsections will explain in detail the experi-mental setup, preprocessing, feature extraction and classifica-tion of the EEG signal.

A. Experimental Setup

EEG signals were recorded using a gMobilab+ console byGuger Technologies that was connected to a computer andcaptured using the Matlab Data Aquisition Toolbox with asampling frequency of 256 Hz. Eight electrodes were placedon the scalp at positions FC3 and CP3, P1 and P5, FC4 andCP6 and P2 and P6 to record the bipolar EEG signal at pointsC3, P3, C4 and P4, respectively. The electrodes were placedaccording to the standard 10–20 international system. Figure 1shows the position of the electrode placement for the bipolarEEG recording.

Data were collected from 10 subjects, in 5 separate record-ing sessions over a course of 2 weeks. All subjects weremale students from the Faculty of Engineering, MultimediaUniversity, whose age ranged from 22 to 28 years old. Subjectswere required to sit on a reclining chair and remain calmand relaxed throughout the whole recording procedure. Eachsession consisted of 2 tasks, i.e., eyes open and eyes closed.Each task was repeated in 5 trial where each trial lasted for 30seconds. Subjects were required to minimize any movementsto avoid contamination to the EEG signal. During signalacquisition, subjects were asked to clear their minds of anythoughts and relax.

Fig. 1. Position of electrode placements for bipolar EEG recording.

B. Preprocessing

Prior to the feature extraction stage, the signal is filteredusing a notch filter with a cut–off frequency at 50 Hz to removeline noise produced by the alternating current of the recordingconsole. Subsequently, a pre–emphasis filter was applied tothe signal to boost the higher frequencies. The EEG signalsare then segmented into 5 second frames with an overlap of50%. Thus, in total, there are 275 frames per channel as shownbelow:

Total Frames = 1 channels × 11 framestrial

× 5 trialssession

× 5 sessions

= 275 frames/channel

C. Feature Extraction

The AR model of order p is defined as

Xt =

p∑i−1

ϕiXt−i + εt (1)

where Xt is the data of the signal at sampled point t, ϕ,...,ϕp are the real valued AR coefficients and εt is white noise.An AR model can thus be viewed as the output of an all-poleinfinite impulse response filter.

The order of the model were gradually increased startingfrom order 3 until the results saturated at order 21. As aresult, order 21 was chosen for the remainder of the tests.The total number of features for each channel is:

275 frames × 21 coefficients/frame = 5775 features.

Figure 2 shows 275 superimposed frames of AR model order21 of 4 test subjects.

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Fig. 2. Spectral plots of 275 frames of autoregressive model order 21 of 4test subjects over 5 sessions on different days.

D. Classifications

The data collected were randomly divided into training andtesting sets everytime the system was executed, where 90%of the data are used for training and the remaining 10% fortesting. The features were then defined as input layers tothe neural network algorithm. Two hidden layers were usedwith 100 nodes on each layer. The training function usedwas the scaled–conjugate gradient. The minimum performancegradient was set to 1.00 e−18 and training will stop when anyone of these conditions are met:

1) the maximum number of epochs reaches 3000;2) the mean square error reaches 0.01; or3) the performance gradient falls below 1.00 e−18.

V. RESULTS AND DISCUSSION

The results of this experiment will be based on the correctrecognition rate (CRR) of the system which is calculatedaccording to the following equation:

CCR =1

10

10∑i=1

Cn

Tn× 100% (2)

where Cn is the number of correct classifications and Tnis the total number of testing samples. Since samples usedfor training and testing are randomized everytime the systemis executed, the CCR is computed over an average of 10executions.

Table I shows the results for eyes–closed and eyes–openusing 1 channel, a combination of channels, and all channels.It was observed that eyes–closed 4–channels gave the bestrecognition rate of 97%, followed by eyes–open 4-channels of96%. Further tests are conducted in order to find out whetherthe CRR decreases with less number of channels.

Combinations of two channels of various positions gaveclassification rates ranging from 90% to 95% which is stillconsidered relatively high. Channels 1 and 2 located on theright hand side of the brain produced the highest classificationrate of 95% for both eyes–open and eyes–closed compared toother combinations. As expected, using only 1 channel resultedin the lowest classification rates ranging from 70% at P3 to87% at C4 for both eyes–open and eyes–closed data.

From the results obtained, the classification rates using 4channels of eyes–closed EEG is slighly better than eyes–open,probably due to less artifacts in the signal, however, statisticalanalysis shows that whether or not the subjects’ eyes wereclosed or open is not significant for any number of channels.One can also observe from Table I that the position of theelectrode plays a vital role in EEG biometrics. If a 2–channelsystem was used, the right brain electrodes i.e., C4 and P4 willprovide the best results. For a 1–channel system, C4 wouldprobably give the best results. In any case, it was observedthat the centre location of the right brain consistently resultedin higher CRRs.

These results corroborate an earlier finding using a differentapproach [1], where for a 2–channel biometric system basedon wavelets, the highest CRRs were for channels C3 and C4in eyes–open and channels C4 and P4 in eyes–closed. The

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TABLE ICCR AVERAGED OVER 10 EXECUTIONS

Channel Area Eyes Opened Eyes ClosedC4 Cerebral 85% 87%P4 Posterior 86% 85%C3 Cerebral 80% 82%P3 Posterior 75% 70%

C4, P4 Right Brain 95% 95%C3, P3 Left Brain 90% 90%C3, C4 Cerebral 94% 93%P3, P4 Posterior 91% 93%

All Cerebral and Posterior 96% 97%

common channel in both cases is channel C4 which is whatwe observed here.

It is important for us to understand why the electrode atC4 produces the highest CRR rates compared to the otherelectrodes. The C4 channel is located at the centre of the righthemisphere, above the right frontal lobe, (see Figure 1). It isgenerally known from scientific studies that the right side ofthe brain is involved with imagination, creativity and feelingwhereas the left brain is more involved with facts and logic.When subjects were asked to “clear their minds and think ofnothing” it is quite clear that the role of the right brain is moresignificant than the left brain. It is speculated that the subjectseach had a unique way of thinking of “nothing” since datacollected over 5 separate days were still able to achieve highclassification rates.

Through visual analysis of the spectra shown in Figure 2,it is quite evident that the 4 people in this example havesignificantly different spectral envelopes from each other yeteach individual manage to generate a consistant signal, eventhough the sessions were conducted over a course of severaldays. However, more research and analysis needs to be donein order to confirm that the EEG is robust for a large databasesimply because it is a dynamic signal. For a small databasehowever (approximately 10 people), the classification rates areexcellent.

Table II shows the amount of time that was taken bythe system to identify the subject for both eyes–closed andeyes–open. It was observed that the average recognition timeis 0.38 seconds which is fast. The training time, which isnot presented here, is considerably longer (approximately 5minutes depending on the number of subjects), however, inimplementation, this is done prior to identification so it doesnot affect the practicality of the system. It is anticipated thatrecognition time may take longer for a significantly largerdatabase.

VI. CONCLUSION

A practical EEG–based biomteric system should use theleast number of channels possible. We observed that for a2–channel implementation, C4 and P4 gave the best CRRrates whereas for a 1–channel implementation, C4 gave thehighest rates because it is more related to imagination andcreativity as opposed to the left brain which is involved withlogic. Visual analysis shows that the spectral envelope of eachperson is unique even though data was taken over severaldays. Recognition time was also quite fast, therefore, there is

TABLE IITHE AMOUNT OF TIME TO IDENTIFY THE SUBJECT (IN SECONDS) FROM

EYES–OPEN AND EYES–CLOSED DATA.

Channel(s) Eyes–Open (sec) Eyes–Closed (sec)C4 0.60 0.42P4 0.30 0.36C3 0.68 0.39P3 0.54 0.59

C4, P4 0.18 0.23C3, P3 0.37 0.16C3, C4 0.61 0.31P3, P4 0.32 0.29

All 0.15 0.34

a possibility of a practical implementation of an EEG–basedbiometric system.

ACKNOWLEDGMENT

The authors would like to thank the Ministry of Science,Technology and Innovation, Malaysia (MOSTI) for fundingour research.

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