Person Identification by Using AR Model for EEG Signals

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1 Perso Idetificatio by Usig AR Model for EEG Sigals Gelareh Mohammadi, Parisa Shoushtari, Beham Molaee Ardekai ad Mohammad B. Shamsollahi Abstract A direct coectio betwee ElectroEcephaloGram (EEG) ad the geetic iformatio of idividuals has bee ivestigated by europhysiologists ad psychiatrists sice 1960 s; ad it opes a ew research area i the sciece. This paper focuses o the perso idetificatio based o feature extracted from the EEG which ca show a direct coectio betwee EEG ad the geetic iformatio of subjects. I this work the full EO EEG sigal of healthy idividuals are estimated by a autoregressive (AR) model ad the AR parameters are extracted as features. Here for feature vector costitutio, two methods have bee proposed; i the first method the extracted parameters of each chael are used as a feature vector i the classificatio step which employs a competitive eural etwork ad i the secod method a combiatio of differet chael parameters are used as a feature vector. Correct classificatio scores at the rage of 80% to 100% reveal the potetial of our approach for perso classificatio/idetificatio ad are i agreemet to the previous researches showig evidece that the EEG sigal carries geetic iformatio. The ovelty of this work is i the combiatio of AR parameters ad the etwork type (competitive etwork) that we have used. A compariso betwee the first ad the secod approach imply preferece of the secod oe. Keywords Perso Idetificatio, Autoregressive Model, EEG, Neural Network I. INTRODUCTION ERSON idetificatio by EEG sigals is oe of the ew Presearch areas i the sciece which ca show a coectio betwee the geetic iformatio ad EEG of a idividual. EEG recordig is o-ivasive ad medically safe; therefore, it should be feasible to use EEG as a useful tool for perso idetificatio. The existece of geetic iformatio i the EEG was ivestigated as early as i the 1930 s [9].However, it has ot bee expaded util i the 1960 s that a direct coectio was established betwee a perso s EEG ad his/her geetic iformatio [21]. Most of the previous G. Mohamadi is a M.Sc. studet i the School of Electrical Egieerig, Sharif Uiversity of Techology, Tehra, Ira ( gelareh_mohamadi@yahoo.com ). P. Shooshatri, is a M.Sc. studet i the School of Electrical Egieerig, Sharif Uiversity of Techology, Tehra, Ira ( pshooshtari@ee.sharif.edu ). B. Molaee Ardekai is with the LTSI, Uiversité de Rees 1 ad Iserm U 642, Rees, Frace, ad the School of Electrical Egieerig, Sharif Uiversity of Techology, Tehra, Ira. ( beham.molaeeardekai@uiv-rees1.fr) M. B. Shamsollahi is a assistat professor i the School of Electrical Egieerig, Sharif Uiversity of Techology, Tehra, Ira. (Correspodig author, phoe: ; mbshams@sharif.edu ). researches have focused o the classificatio of geetically or pathologically iduced EEG variats due, for example to epilepsy or schizophreia for diagostic purposes,[12]-[20]. O the cotrary, the preset work focuses o healthy cases ad aims to establish a oe-to-oe correspodece betwee the geetic iformatio ad certai appropriate features of the recorded EEG sigal of idividual. A direct coectio betwee geetic iformatio ad EEG says that EEG must be uiqueess for each perso. Although much ivestigatio has ot bee doe to assess the uiqueess of EEG patters of each perso i the rest, there are some proofs showig that EEG patters are probably uique for idividuals [1]. I this research, it has bee tried to fid out suitable EEG features as biometrics to classify idividuals by employig a competitive eural etwork. I the sequel of this sectio there are brief expressio about EEG data ad biometrics. A. EEG data Brai waves (EEG) are the resposes of the eural cells to various stimuli [2]; these waves, o the surface of the brai, are resposes to differet stimuli ad what is recorded is the sum of all these resposes. There are some electrodes o the scalp to record ad amplify sigals. These electrodes are typically placed i stadardized locatios over the mai aatomical structures of the brai such as: Frotal, Temporal ad Parietal lobs [3]. EEG sigal is a time series which has a statistical properties but these properties are varies by meas of time, metal state ad differet persos. B. Biometrics Ay biological or physiological sigal like figerprits, retial scas or speech matchig [5] that ca be used to idetify a perso [4] is called biometric. A biometric system uses recogizable features, possessed by a perso. I this paper we use EEG sigal as a idetifyig sigal. The features extracted are AR parameters i specific time duratios ad these features are give to a Competitive Neural Network to be classified. So i this paper AR parameters of EEG is biometric. II. MATERIALS A. Autoregressive Model I this model the series is estimated by a liear differece PWASET VOLUME 11 FEBRUARY 2006 ISSN WASET.ORG

2 equatio i time domai: p X ( t) a( i) X ( t i) e( t) (1) i 1 Where a curret sample of the time series X(t) is a liear fuctio of P previous samples plus a idepedet ad idetically distributed (i.i.d) white oise iput e(k) [6]. I this work the Yull-Walker approach has bee employed to estimate the AR parameters by the use of LMS (least square method) criterio. B. Competitive Neural Network I a competitive eural etwork oly oe of the output euros, havig the highest level, will wi the competitio. I this paper, it has bee used a reiforced learig algorithm. I this approach there is a supervisor determiig the wier euro for the traiig set but it does t determie the real quatity of expectative output so this etwork is cosidered as a usupervised oe. Here is the error sigal biary (zero or oe) for example if the expectative euro became the wier, error sigal is zero otherwise, error sigal is oe ad the etwork will try to adjust its weight till the expectative euro wi. I this etwork there are N euros i the output layer ad each euro has its ow weights (W ).(fig.1) X 1 X 2 X M Fig.1 Competitive Neural Network If X vector is give as a iput data, the output for each euro is calculated by: O W X (2) j j j W 11 W MN W 21 WM1 W 2N W 1N The euro which has the maximum quatity is the wier. Whe the correct euro does ot wi the competitio the sigal error is existed ad the weight matrix of correct euro will be reiforced by the followig rule but the others do t have ay chage: O 1 O 2 O N W ( k) W ( k 1) ( k)( X W ) (3) Where (k) is usually a small positive umber, called traiig coefficiet, this parameter ca chage durig traiig time or it ca be costat. C.. Data Set The used data i this paper have bee recorded from te healthy voluteer subjects. (Six me ad four wome) For each perso the data is take from 100 chaels i 24 sec duratio ad the rate of samplig is 170 Hz. So for each chael there are 4080 samples which are divided to eight epochs of 3 sec duratio for each oe. III. APPROACHES I this paper two methods have bee proposed for feature vector costitutio, sigle chael ad multichael method. There is a little differece betwee these two approaches but a perfectly clear differece ca be see i the results. I the sequel, both of the methods ad the results are preseted. A. Sigle chael Method We wat to demostrate that the EEG of a perso is probably uiqueess which meas that, as explaied before, there is a coectio betwee the EEG ad the geetic iformatio of idividuals. I this research we have 10 subjects ad there are eight epochs of 3 sec duratio for each perso o 100 chaels; so there is a data set of 80 epochs for each chael. The goal of this research is to extract some features which have a discrimiat property i idividuals. Here the AR parameters of each epoch are cosidered as features. Although EEG is ot a statioary sigal i its ature but we assume it statioary i each epoch ad calculate the parameters for each epoch. Depeds o the order of model, the umber of parameters is differet. Due to a specific order, the parameters are calculated. I the sigle chael method, the obtaied parameters of each epoch are arraged i a vector ad this vector is cosidered as the feature vector. A portio of these vectors is give to a supervised eural etwork as the traiig set ad after traiig period, to validate the classificatio ad similarity of EEG parameters i a same idividual, total vectors are give to the etwork as testig set. The applicate etwork is a competitive eural etwork with a reiforced learig algorithm. I the curret approaches, 50 vectors of 80 for each chael are used as the traiig set. This processig was carried out i Msoftware ATLAB o a Petium four PC. I the preseted experimets, oe of the 100 chaels was selected ad the processig was performed o the each perso epochs of the selected chael, the these epochs divided to two parts as the traiig ad testig set, as poited out before. PWASET VOLUME 11 FEBRUARY 2006 ISSN WASET.ORG

3 Here we did t exaimate the effect of chael place o the results accurately but a visual ispectio yield that there is a higher correctess score i the back of the scalp over the parietal chaels tha is i the other chaels.(e.g. here chael umber 004 ad 080). Classificatio scores accordig to the differet orders of AR model for a umber of radom selected chaels are showed i Table 1 ad Table 2. So briefly, at first the AR parameters with a particular order is calculated for each epoch ad the feature vector is formed by these parameters; the by the use of a classifier, which is a Competitive Neural Network i this research, the feature vectors are classified. After traiig the etwork with all the traiig set the etwork will be tested ad the percetage of correct idetificatio is calculated. TABLE 1 SCORES OF SINGLE CHANNEL METHOD Chael Percetage of correct idetificatio accordig to differet order for chael 001, 002, 003, 004 acquisitio of AR parameters for each chael accordig to a specific order, the parameters of two, three or more chaels are assiged i oe vector as the feature vector. Ad as aforesaid, the etwork is traied by a portio of vectors set ad tested by the total vectors. I the preseted experimets like the sigle chael method the etwork use 50 vectors for traiig. The scores of this approach accordig to differet orders of model for a umber of chaels are showed i Table 3 ad Table 4 for a combiatio of two radom selected chaels parameters ad i the Table5 ad Table 6 for a combiatio of three radom selected chaels. These results obviously imply higher scores tha do i the sigle chael method results. So, we ca say that a combiatio of parametric model of EEG i differet chael shows a higher relatio betwee EEG ad geetic iformatio of a perso tha usig oe chael parameters. With a little attetio to the order of AR model ad the scores it is clear that if the order of model icreases to a certai value (here, of order 11), the scores becomes better, but icreasig more tha this value approximately has o effect o the scores. It seems that the value of appropriate order depeds o the umber of subject; ad by icreasig the umber of subject, the algorithm eeds to a higher order to have satisfactive results. Although, calculatig high order AR model parameters for a much umber of subjects, i order to idetifyig them, may ot be reasoable or practical, but the oly purpose i this paper is to show the potetial of this parameters to classifyig persos ad demostratig the probability of EEG uiqueess for idividuals which reveal a direct relatio betwee geetic iformatio ad EEG. Chael TABLE 2 SCORES OF SINGLE CHANNEL METHOD Percetage of correct idetificatio accordig to differet order for chael 014, 030, 050, 080 B. Multichael Method I this approach all the data are like previous, i the sigle chael method, but we tried to itroduce a feature vector with better score i classificatio. Here we use AR parameters as the features agai, but the formed vector is a combiatio of AR parameters of differet chaels. It meas, after IV. CONCLUSION Perso idetificatio based o AR parameters extracted from EEG is addressed i this work. A eural etwork classificatio was performed o real EEG data of healthy idividuals i a attempt to experimetally ivestigate the relatio betwee a perso s EEG ad geetically-specific iformatio. I this paper two methods have bee proposed; first a sigle chael method which uses the AR parameters of oe chael as a feature vector ad secod a multichael method which uses a combiatio of the AR parameters of differet chael as a feature vector. These approaches have yielded correct classificatio scores at the rage of 80% to 95% for the first method ad at the rage of 85% to 100% i the secod oe. Obviously it ca be see that combiatio of the AR parameters from differet chaels improve the score ad if the umber of chael, combied, icreases there is a visible amedmet i the percetage of correct classificatio. These results are i agreemet with the previous researches showig evidece that the EEG carries geetic iformatio, ad also show the potetial of our approach to classify kow EEGs. Certaily, extesive experimetatio is required i order to obtai statistically sigificat results ad thus prove the cojecture of the europhysiologists about the oe-to-oe correspodece betwee the EEG ad the geetic code. PWASET VOLUME 11 FEBRUARY 2006 ISSN WASET.ORG

4 The total result shows that the results i the back of the scalp over the parietal chaels have a better idetificatio tha do i the other locatios of the scalp. It ca also be see that by icreasig order of model more tha 11 i this results it is t a specific chage i percetage of correctess i the curret experimets with 10 subjects; but for more subjects, it seems that probably the least suitable order of AR model is higher. Although, calculatig high order AR model parameters for a much umber of subjects i order to idetifyig may ot be reasoable or practical, but the oly purpose i this paper is to show the potetial of these parameters to classifyig persos ad demostratig the probability of EEG uiqueess for idividuals which reveal a direct relatio betwee geetic iformatio ad EEG. This team i its recet works tries to recogize EEG sigals of a idividual, recorded i distict time, amog others, ad they reach to rather desirable results which will be spread soo. Chael TABLE 3 001, , , , Percetage of correct idetificatio accordig to differet order. (Combiatio of two chaels) TABLE 4 Chael 004, , , , Percetage of correct idetificatio accordig to differet order. (Combiatio of two chaels) Chael TABLE 5 001,002, ,004, ,014, ,030, Percetage of correct idetificatio accordig to differet order. (Combiatio of three chaels) Chael TABLE 6 030,040, ,060, ,060, ,090, Percetage of correct idetificatio accordig to differet order. (Combiatio of three chaels) REFERENCES [1] A. Kasmia (Neurologist), Persoal Comuicatio, Oct Iteret [2] R.Schmidt, G.Thews Itegrative fuctio of Nervous System, Spriger Verlag,Birli1983. [3] A.Remod(editor) EEG iformatics. A didactic review of methods ad applicatios of EEG data processig, Elsevier Sietific Publishig Ic.,New York,1997. [4] A.K.Jai (supervisor), Biometrics Homepage Michiga State Uiversity,. [5] R.Parajape,J.Mahovsky,L.Beediceti, O AR model ad other metrics of the EEG for Subject idetificatio, Iteret. [6] S.Jai,G.Deshpade, Parametric Modelig of Brai Sigals, Iteret. [7] S.Y.Kug, digital Neural Netwok, (PTR Pretic Hall,Eglewood Cliffs,New Jersey 1993) [8] M.Poulos, M.Ragoussi, N.Alexadries, Neural etwork based perso idetificatio usig EEG features, Proceedig IEEE of the iteratioal coferece o Acoustic, Speech,ad Sigal Processig, ICASSP 99,Arizoa,USA,March 1999,PP [9] A. Aokli, O. Fisher, Y. Mao, P. Vogt, E. Schalt, F. Vogel,(1999), A geetic study of the huma low-voltage electroecephalogram, Huma Geetic,vol. 90,PP ,1992. [10] M. Poulos, M. Ragoussi, V. Chrissicopoulos, A. Evagelou, perso idetificatio based o parametric processig o the EEG, Processigs IEE of the Sixth Iteratioal Coferece o Electroics, Circuits ad Systems, ICECS 99, Pafos, Cyprous. September 1999, PP [11] M. Poulos, M. Ragoussi, V. Chrissicopoulos, A. Evagelou, parametric perso idetificatio from the EEG usig computatioal geometry, Processigs IEE of the Sixth Iteratioal Coferece o Electroics, Circuits ad Systems, ICECS 99, Pafos, Cyprous. September 1999, PP PWASET VOLUME 11 FEBRUARY 2006 ISSN WASET.ORG

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