A Pervasive EEG-based Biometric System

Bin HuSchool of Computing,

Telecommunications andNetworks, Birmingham

City UniversityBirmingham, UKbin.hu@bcu.ac.uk

Chengsheng MaoSchool of Information

Science and Engineering,Lanzhou University

Lanzhou, Chinachshmao@gmail.com

William CampbellSchool of Computing,

Telecommunications andNetworks, Birmingham

City UniversityBirmingham, UK

William.Campbell@bcu.ac.uk

Philip MooreSchool of Computing,

Telecommunications andNetworks, Birmingham

City UniversityBirmingham, UK

ptmbcu@gmail.com

Li LiuSchool of Information

Science and Engineering,Lanzhou University

Lanzhou, Chinaliliu@lzu.edu.cn

Guoqing ZhaoSchool of Information

Science and Engineering,Lanzhou University

Lanzhou, Chinazhaogq10@lzu.edu.cn

ABSTRACT

Identification of individuals is ubiquitous with increasing re-liance by financial and governmental organizations on reli-able and robust personal recognition systems to determineand confirm the identity and policy constraints in ’real-time’when reacting to service requests. Deficiencies in tradition-al approaches to user validation are becoming increasinglyapparent in the current information-oriented society. Withdevelopments in research into the human brain, biometricmethods based on brain wave signals, as an effective ap-proach to user validation, have received increased attentionsince an individual’s brain wave signals cannot be dupli-cated, discarded or stolen. Targeting at pervasive systemsand the identified deficiencies in traditional approaches touser identification and validation, an electroencephalogram(EEG)-based biometric system for use in pervasive environ-ments is proposed in this paper. A significant problem ofEEG-based biometrics in pervasive environment is the re-quirement of real-time and convenience. In our study, onlyone active electrode with a portable EEG collection devicewas used and no other instructions to users for convenience;in addition, the signal analysis methods we used were ef-ficient to achieve less time consumption. In our prototypesystem, 11 subjects were identified with recognition accura-cy in the range 66.02% to 100%; the recognition accuracyincreased with increases in the EEG sample time; and thecomputational time of signal analysis was about 0.5s. Thelow computational time of EEG data analysis validates thismodel when implemented in pervasive environments. For d-iffering applications we can define a suitable balance point

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to optimize the conflicting demands of data collection timelength and recognition accuracy.

Author Keywords

EEG, biometrics, individual identification, pervasive system.

ACM Classification Keywords

H.5.m Information interfaces and presentation: Miscellaneous.

General Terms

Design, Experimentation, Human Factors, Measurement, Per-formance, Verification.

INTRODUCTION

Recently, the information-oriented society has gained trac-tion. Accordingly, increasingly effective security checkingand validation procedures are demanded to prevent data lossand mitigate unauthorized access to information systems toimprove overall security provision. A reliable and robust se-curity system is critical where controlled access is required;examples of such applications include secure access to build-ings, computer systems, laptops, cellular phones, ATM’s,and Software-as-a-Service (SaaS) systems. With develop-ments in brain related research, biometric methods basedon brain wave signals have received increasing attention asbrain wave signals cannot be duplicated, discarded or stolen;EEG-based biometric profiling therefore offers great poten-tial for increased security. In this paper, electroencephalo-gram (EEG) user profiling is used to identify individuals inpervasive environments.

Electroencephalogram

Electroencephalogram (EEG) profiling uses the electrical ac-tivity of the neurons in the brain; when the neurons are ac-tive they produce an electrical potential. The combinationof this electrical potential of groups of neurons can be mea-sured outside the skull using a non-invasive technique. Each

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Figure 1. The 10-20 electrode placement system. Electrodes we used

are indicated with colors (red indicates the active electrode and blue

reference electrodes).

individual has a unique brain neuron configuration; there-fore spontaneous EEG profiles between individuals will bedifferent [20]. The use of EEG represents a potentially ef-fective solution to realizing identification and validation ofindividuals.

To obtain the EEG measurements an array of electrodes isplaced on the scalp. The electrodes are placed according tothe international 10-20 system [11] as is depicted in Figure1. This system makes results from different research projectseasily comparable.

In order to collect EEG conveniently, a variety of wearableEEG caps with varying number of electrodes connected to aportable EEG collection device has been developed, whichmakes the implementation of pervasive EEG-based biomet-ric systems possible. Figure 2 shows a wearable EEG capand Figure 3 shows a portable EEG collection device; thesemay be used in pervasive environments. In our experimen-t, we used only one active electrode (Cz) and two refer-ence electrodes (A1, A2) as identified in Figure 1; and theportable EEG collection device is a Nexus-4, shown in Fig-ure 3.

Biometrics

In the absence of robust personal recognition schemes, thetraditional individual identification systems are vulnerable tounauthorized access. Robust personal recognition schemesshould not be only based on ”what he/she possesses” (e.g.an ID card or a key) or ”what he/she remembers” (i.e. apassword). Traditional schemes such as ID cards and pass-words have their own limitations; ID cards or other physicalevidence of identity can be lost, stolen or not carried on theperson. Effective passwords are generally too long and ran-dom; it is difficult to mitigate the potential for hacking using’brute-force’ search techniques. Clearly, remembering sucha password is not practical and carrying the password on the

Figure 2. A wearable EEG cap

Figure 3. A portable EEG collection device

person (as is often the case) represents a severe security risk.

Biometric profiling offers the potential to establish and con-firm an individual’s identity based on ”who he/she is”, ratherthan by ”what he/she possesses” or ”what he/she remember-s”. A number of biometrics have been used to identify indi-viduals; typical biometric approaches include: (1) fingerprint-based techniques [13], (2) facial feature recognition [5], (3)palm print recognitions [8, 12], (4) hand geometry recog-nition [12], (5) iris recognition [9], and (6) voice recogni-tion [21]. Although these approaches play an important rolein the development of individual identification systems, theuse of such systems involve a number of disadvantages in-cluding: (1) there are potential vulnerabilities in that someof these biometric features can be mimicked or duplicatedby imposters, and (2) some of the physical body parts whichinclude these biometric features are liable to be impaired.

To address the issues identified the EEG-based biometricsis emerging as a potentially effective solution to user iden-tification. It is currently impractical for brain activity to beduplicated or stolen given the current ’state-of-the-art’ de-

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velopments in science and technology. In addition, EEG fea-tures are universal and the brain will always produce brainwave activity unless life terminates. Moreover, EEG record-ing can be non-invasive, medically safe, and can be detectedunconsciously. This makes continuous identification possi-ble. Therefore, EEG-based biometric profiling approachesoffer significant benefits over traditional security systems.

Related Researches

There is a significant body of published research addressingbrain electrical activity to enable identification of individ-uals. Research aiming to extract genetic information fromhuman brain electrical activity began as early as 1930’s [2],however the first results became available only after 1970s[24]. Their objective was to extract more or less invariantcharacteristics of brain signals that would characterize theindividual.

Recently, an increasing number of studies [18, 19, 17, 17,16, 15, 14, 20] relating to EEG-based individual identifica-tion have emerged, each with their own limitations. In paper[18, 19, 17], Poulos et al attempted to differentiate subject-s individually from a pool of different individuals by EEG.In [18], they proposed methods using non-parametrical fea-tures of real EEG signals and learning-vector-quantization(LVQ) neural network (NN) to recognize an individual asdistinct from other individuals. The accuracy 80%–100%(case-dependent) was obtained based on the experiments in-volving four subjects and 255 EEG patterns.

Paranjape et al [16] examined the use of AutoRegressive(AR) models of various orders computed from EEG signal-s recorded from the subjects with eyes open or with eyesclosed. They examined 349 EEG trials from 40 subjects,and the subsequent employed discriminant analysis gave theclassification accuracy ranged from 49% to 85%, however,the variation range of the accuracy may be too wide and theaccuracy is not satisfactory for our proposed approach.

Marcel et al [14] proposed the use of a statistical frameworkbased on Gaussian Mixture Models and Maximum A Poste-riori model adaptation, showing that there were some mentaltasks that were more appropriate for person authenticationthan others. Mohammadi et al [15] proposed a method us-ing AR parameters as feature vector and a competitive neu-ral network to identify individuals and got a classificationscores at the range of 80% to 100%. Riera et al [20] present-ed a rapid and unobtrusive authentication method that onlyused 2 frontal electrodes referenced to another one placed atthe ear lobe using multistage fusion architecture. An equalerror rate (EER) of 3.4% was obtained based on an experi-ment with 51 subjects and 36 intruders.

These research projects provide support for the conclusionthat EEG carries genetic information; this provides an exper-imental basis for our study. There is however limited docu-mented research which considers the sample time, compu-tational time and data storage when undertaking individualidentification; these are significant for applications in perva-sive environments. Furthermore, the EEG collection proce-

dures in previously documented research are cumbersome.Accordingly, these approaches are not generally practical in’real-world’ situations.

Our Work

Targeting the disadvantages of the previously identified meth-ods, this paper proposes an EEG-based biometric system foruse in pervasive environments. In our study: (1) the EEGcollection device is portable and wearable, and (2) only oneactive electrode is involved without any other instruction;this makes the EEG data collection more convenient and po-tentially useful in a greatly enhanced range of locations, en-vironments, and situations. Last but not least, the results ofour experiments support the conclusion that individuals canbe effectively identified.

In our biometric profiling system, the EEG data was collect-ed using a Nexus-4 (a portable and wearable device) with anelectrode located on the Cz point on the scalp (see Figure1). The data is transmitted to a smart device (in our casea mobile phone) using a Bluetooth connection. The smartdevice received the raw EEG signals (data) and carried outsome data processing; the processed signals (the informa-tion) was then sent to a server via the Internet. The serverwould search the database to find the individual to whom theunique EEG profile belongs and then send the informationrelating to that person to the smart device.

A prototype system was implemented based on this biomet-ric method. And 11 users have tested the performance ofour prototype system. The recognition accuracy ranged from66.02% to 100% roughly rising with the increase of the EEGsample time and the computational time of data analysis isabout 0.5s, which would validate this system to pervasiveenvironments.

Figure 4 shows some applications of this system in perva-sive environments. An application sends EEG signals to ascheduling server via Internet. The scheduling server willdispatch data for different application to different data anal-ysis servers. The analysis server will search the correspond-ing database to identify individuals and give a feedback tothe application. An EEG-based payment system will asso-ciate a user’s EEG with his/her credit card number and pass-word, which will releases users from carrying credit card andremembering the password. An EEG-based ID certificationsystem can identify individuals through EEG, so ID cardswill be released from being carried about. An EEG-basedgate control system will effectively prevent unauthorized ac-cess. For an EEG-based e-commerce system, the users canconfirm each other’s identification through the EEG-basedbiometric system; so it can effectively prevent internet fraud.

Scenario

This biometric system can be designed for the security andvalidation demands for a range of domains and systems. Forexample, Bob needs to travel on business by air. Upon arrivalat the airport, his identification must be checked against ter-rorist watch list. Using traditional methods, the airport staffwill check his ID card and the air ticket to confirm the iden-

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Figure 4. EEG-based biometric system in pervasive environments

tity. Using the system proposed in this paper Bob only needsto attach an electrode to his scalp for a moment. The EEGsignal is collected and undergoes a series of processing andtransmission step. Then his information will be returned tothe smart device. Using this information, if Bob does nothave the permission to enter the departure lounge, the sys-tem will trigger an alarm. While if Bob is validated usingthe EEG data, the system opens the gate to welcome him.This biometric system would release passengers from carry-ing their ID card and air ticket, increase security, and avoidthe need to check the authenticity of the information on theID card.

When feeling thirsty in the departure lounge, Bob goes to aconvenience store to buy some drink. But he has forgottenwithdrawing any cash with him and has to pay by credit card.But the password of his credit card is also forgotten. In thatcase, Bob can collect his EEG and send it to the server ofbank, the information of all his credit cards (including cardnumbers and the corresponding passwords) will be returnedto his mobile phone. This system would release users fromcarrying cash and remembering a long password.

THE EEG-BASED BIOMETRIC SYSTEM

To enable the application of our EEG-based biometric profil-ing approach in pervasive environments, we have designeda biometric system. The EEG-based biometric system canidentify individuals according to their EEG profile anytimeand anywhere. It includes the following 3 modules: (1) the

Figure 5. The EEG-based biometric system design for pervasive envi-

ronments

EEG collection module, (2) the front-end display module,and (3) the background processing module. The design ofthis system is depicted in Figure 5.

The EEG collection module mainly implements the EEGcollection device; it collects EEG data from a user and thensends the EEG data to a smart device (i.e. the front-end dis-play module) through a Bluetooth connection. As soon as

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the front-end display module receives the EEG signal, it dis-plays this signal to the user on the smart device (in our casea mobile phone). However, due to hardware resource con-straints, the smart device cannot effectively undertake thecomplex tasks of processing the EEG data, thus the smartdevice in our system is a thin client and the data processingis delegated to a server which is needed to process and ana-lyze the EEG data as a background process. The smart de-vice sends the received EEG data to a server using wirelesstransmission technologies such as GSM, GPRS, and CDMAetc. The background processing module, a server, will de-noise the received EEG signal and then extract certain fea-tures from it. To identify the individual the received EEGdata belongs to, it connects to the database and executes aclassification algorithm taking the EEG data in database astraining data. After classifying the received EEG data, theserver returns the feedback information (identity informa-tion or no matched information) to the smart device. Thesmart device displays the information on the screen to theuser and the system will make corresponding decisions ac-cordingly. If no matched information is found, the user maybe suggested to register to the corresponding database.

SYSTEM IMPLEMENTATION

We have implemented a prototype system according to thedesign above. In our study, raw EEG signals were denoisedto remove the undesired signals such as electro-oculogram(EOG), and then certain features were extracted from the de-noised EEG signals. The classification algorithm was thenemployed to classify the signals and estimate the recognitionaccuracy according to the extracted features. In our program,we have also recorded the computational time for each step.The procedures of our study are illustrated in Figure 6.

The Data Collection

In our prototype system, EEG signals were recorded on aNexus-4 with one active electrode located at Cz position andtwo reference electrodes located at A1 and A2 (the two ear-lobes) according to the International 10-20 system, shown inFigure 1. The sampling rate was 256 Hz.

The Signal Preprocessing

Raw EEG signals are notoriously noisy and difficult to an-alyze and features extracted from these raw data would notbe robust and reliable enough for further analysis, thereforeprior to the data being used in our program it has to be pre-processed. One important preprocessing step is the removalof noise from the signals. Because the electrical activity ofthe brain is produced in the order of micro volts and thesesignals are very weak with a large amount of noise. The p-resence of noise can be due to external and internal causes;the external causes include static electricity and electromag-netic fields produced by surrounding devices. In addition tothese external causes, the EEG signals are also heavily in-fluenced by the internal causes, artifacts that originate frombody movement or EOG included.

The noise present in the EEG signals can be denoised us-ing simple filters and wavelet transformation. In our study,a 40Hz low-pass filter was used to remove higher frequency

interference such as noise from the electrical net which hada fixed frequency of 50Hz. Then the 7-order Haar wavelettransformation was used to detect and remove electro-oculogram(EOG) by soft-thresholding [7]. The denoised signals weresufficiently pure enough to analyzed. The alpha (8-13Hz),beta (14-30Hz) and theta (4-7Hz) rhythm were extracted fromthese denoised signals by finite impulse response (FIR) band-pass filters. Signals in other frequency bands were not takeninto account in our study.

The Feature Extraction

After the alpha, beta and theta rhythms were extracted fea-ture extraction on the signals was performed in the followingstage. The features extracted serve as unique descriptors ofperson’s brain activity. In our study, we extracted 9 features(center frequency, maximum power and sum power of eachrhythm) using AR models [22]. These features were the onesthat provided the input for our classifiers.

An AR model is an example of single channel signal pro-cessing. The signals are processed for each channel with-out taking into account the other channel. There are severalrelated studies [19, 16, 15, 20] where signal processing in-volved an AR model. In AR models the time series are esti-mated by a linear difference equation in the time domain. Asingle channel EEG signal can be regarded as being gener-ated by a certain system which is stimulated by white noise.We can deal with the relationship between the input and theoutput of the system as long as the parameters of the systemare known. The representation of AR model is:

X(t) =

p∑

i=1

aiX(t− i) + E(t) (1)

where a current sample of the signal X(t) is a linear functionof p previous samples plus an independent and identicallydistributed (i.i.d) white noise inputE(t). p is called the orderof the AR model.

The function aryule in MATLAB was applied to build an ARmodel. To build an AR model, the order of an AR modelshould be considered first. In our study, Akaike’s informa-tion criterion (AIC) rule [4] was used to estimate the order ofan AR model. The order of an AR model with the minimumAIC was selected. The representation of AIC is:

AIC = ln δ2 + 2p/N (2)

where δ2 is the variance of white noise, p is the AR modelorder and N is the length of signal X .

If the coefficients of AR model have been calculated, thepower spectrum can be obtained by the following equation[1]:

Γ(f) =δ2

|1 +∑p

k=1ake−2jkfπ |2 (3)

where ak and δ2 can be solved out from equation (1).

From equation (3), Γ(f) can be regarded as a function off , we can solve the maximum value of Γ(f) and the cor-responding f , which serve as the two features (maximum

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Figure 6. The procedures of our study. The results of the classification are identity information.

power and center frequency). The sum power can be ob-tained by computing the integral of Γ(f) with respect to fin corresponding frequency band.

Classification

The features of EEG are rich in hidden information that canbe used to identify individuals. Classification is one of themost important ways of data mining that is used to searchthe regularity of a vast amount of data. For our study, theclassification aims to find the right person who owned thecurrent EEG signal.

The features obtained in feature extraction step are used asfeature vectors for classification. These vectors are fed intoa classifier, first for training and then for the actual classi-fication of unknown input vectors. In our study, we haveimplemented naive Bayes classifier (NBC) for its high effi-ciency and low computational overhead. The EEG signalswere classified by the classifier according to the features ex-tracted. Here we give a brief introduction to them.

The construction of the NBC does not require any compli-cated iterative parameter estimation schemes. This mean itmay be readily applied to huge data sets. This is significantto pervasive environments. General discussion of the NBCmethod and its merits are given in [6, 10].

Bayes classifiers are based on Bayes theorem. A Bayes clas-sifier can predict the class membership probabilities, such asthe probability that a given tuple belongs to a particular classand then classifies the tuple into the class that it belongs tomost probably.

In our study we also take the NBC into consideration to clas-sify the EEG signals. Before using the NBC, we assume thatthe features extracted from the EEG signals are conditional-ly independent and each feature has a Gaussian distributionwith a mean µ and standard deviation σ, defined by

g(x, µ, σ) =1√2πσ

e−(x−µ)2

2σ2 (4)

In our study, the NBC computed the probability that a testtuple belongs to each target class; then the classifier find outthe maximum probability and the corresponding class label;the test tuple was classified into this class.

EXPERIMENT AND RESULTS

We have implemented the prototype system with the clienton Java 2 Micro Edition (J2ME) platform and the server onJava 2 Standard Edition (J2SE) platform. We have testedthe performance of the prototype system by 11 users. In

our experiment, the database includes all the 11 users’ EEGfeatures which were collected before. Then the users use thissystem to identify themselves through their current EEGs.Each user has tested this system twice to get two sessions,one in morning and the other in afternoon. Each session ofa subject lasts 2 minutes and is splitted into epochs. Eachepoch is considered as a sample.

The recognition accuracy is closely related to sample time. Itis easily imagined that the longer EEG signal includes moreidentity information, which is also verified in our experimen-t results. The variation of recognition accuracy with EEGsample time in our experiment is shown in Figure 8, fromwhich we can see that the accuracies roughly rise with theincrease of EEG sample time. For a certain application, ac-cording the requirement of accuracy and sample time, wecan choose a balance point (a trade-off) between recogni-tion accuracy and sample time. For example, in an entrancesystem of a confidential government department where highlevels recognition accuracy is required, we can sacrifice somesample time for higher recognition accuracy. But in occa-sions requiring ’real-time’ property access where lower lev-els of security and lower time consumption are demandedwith commensurate reductions in computational overhead,some recognition accuracy would be sacrificed to save time.This method could be developed into a unimodal identifica-tion system or combined with other methods to form a multi-modal identification system.

The kappa statistic was then used to measure the proportionof agreement between a classifier and the gold standard withcorrection for chance. In our study, the gold standard indi-cated the correct classification result. Kappa scores rangingfrom 0.4-0.6 are considered to be fair, 0.6-0.75 are good, andscores greater than 0.75 are excellent [23]. Our statisticalvalue of kappa varied with EEG time length range from 0.6to 0.9. This shows a good or excellent agreement of recog-nition results.

In our experiment, the time consumption of every procedurewas considered for application to pervasive environments.For a measurement, the signal preprocessing elapsed 191ms,the feature extraction elapsed 278ms and classification 31m-s. Besides the EEG signal collection time and the networktransmission delay, all the computational time was about 0.5second, which had advantages in pervasive environments.

CONCLUSION AND FUTURE WORK

In this paper, we have proposed an EEG-based biometricsystem for use in pervasive environments. Our proposedsystem can collect EEG data and analysis it to identity in-

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Figure 7. The recognition accuracy varied with EEG sample time

dividuals in ’real-time’. In this method, The EEG signal-s were processed by data preprocessing, feature extractionand classification. We proposed the use of an AR modelin feature extraction and implemented using a NBC to en-able EEG data classification. The results obtained from theexperimental study involved 11 users gave recognition accu-racy between 66.02% and 100% and the computational timeof data analysis about 0.5s, which makes its application topervasive environments a potentially viable solution to useridentification.

However, the database we used is still small and no definiteconclusive relation can be learned for the task of individu-al identification from the results reported here. We plan tocollect a more appropriate database with more subjects, andwhere various real-world scenarios and mental tasks will beinvestigated to be more convictive and more pervasive.

The time consumption of individual identification using thissystem is mainly in the data collection procedure, becauselong EEG sample should be collected to improve recognitionaccuracy. In order to save EEG collection time to make thissystem more pervasive and more effective, features of shortEEG sample that can identify individuals should be extractedin our future work.

As for the approaches involved in our method, we may searchother feature extraction approaches to extract additional fea-tures and then employ feature selection algorithms to selecta group of related features to recognize individuals in ourfuture work. Moreover, more effective denoising algorithmand classification algorithm will be applied to our work toimprove the recognition accuracy.

Finally, more extensive experimentation is necessary, in or-der to obtain statistically significant results and thus verifythe conjecture of the existence of a one-to-one correspon-dence between the EEG and the genetic code of an individ-ual.

ACKNOWLEDGEMENTS

This work was supported by the National Basic ResearchProgram of China (973 Program) (No.2011CB711001), theNational Natural Science Foundation of China (grant No.60973138), the EU’s Seventh Framework Programme OP-TIMI (grant No.248544), the Fundamental Research Fund-s for the Central Universities (grant No. lzujbky-2009-62),the Interdisciplinary Innovation Research Fund For YoungScholars of Lanzhou University (grant No. LZUJC200910).

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