EEG Feature Selection for Thought Driven Robots using Evolutionary Algorithms

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1 EEG Feaure Selecion for Though Driven Robos using Evoluionary Algorihms Kasun Amarasinghe, Parick Sivils, Milos Manic Deparmen of Compuer Science Virginia Commonwealh Universiy Richmond, Virginia, Unied Saes Absrac Machine conrol using elecroencephalography (EEG) based brain compuer inerfaces (BCI) has been exensively researched in he pas decade. However, research is ofen based on even bound mehods such as moor imagery. Despie being useful in medical applicaions, even bound mehods limi users operaional capabiliy while performing BCI conrol. To alleviae he said limiaion, we explore a robo conrol framework based on absrac hough. Absrac hough in his conex is defined as conscious menal asks ha are no bound wih any paricular even or bodily movemen. This paper presens an iniial sep in he framework, which is a mehodology for opimal feaure selecion for absrac hough EEG daa classificaion. The presened mehod conains 2 seps: 1) generaional Geneic Algorihm (GA) based feaure selecion, and, 2) EEG daa classificaion using seleced feaures. The presened mehod was implemened on an EEG daase acquired from a consumer grade EEG device. Absrac hough EEG daa were colleced for hree acions peraining o robo conrol; 1) res, 2) move forward, and, 3) urn lef. The presened mehod was compared o EEG classificaion wihou any feaure selecion. Experimenal resuls showed ha he presened mehod ouperformed he mehod wihou feaure selecion for all he esed classifiers wih a 10% or higher improvemen in classificaion accuracy. Keywords Brain Compuer Inerfaces; Classificaion; Arificial Neural Neworks; Geneic Algorihms I. INTRODUCTION Brain Compuer Inerfaces (BCI) are human-machine sysems in which commands are inerpreed solely from a user s measured brain aciviy [1], [2]. This aciviy can be colleced in a myriad of ways, including measuring he elecric field (EEG) or he magneic field (MEG) of he brain, funcional magneic resonance imaging (fmri), and funcional near-field infrared specroscopy (fnirs) [3]. For pracical research, EEG signals have been he preferred mehod of measuremen due o low cos, ease of use, and minimal echnical requiremens [4], [5]. Furhermore, he exensive and ongoing research in EEG based BCI, as well as curren echnological developmens, have yielded he developmen of low cos consumer grade EEG devices ha can be used in he implemenaion of EEG-BCI sysems [6], [7], [8]. One promising applicaion of EEG-BCI is using EEG signals as menal commands for robo conrol [1], [3]. Robo conrol hrough EEG-BCI sysems has been shown o be usable in differen domains including medical applicaions, enerainmen, manufacuring and securiy [2], [3], [10]. For EEG-BCI sysems, researchers ofen base heir models on mehods ha are bound o paricular evens [11], [2], [12]. Two popular examples are moor imagery and P300 Even Relaed Poenial (ERP) [11], [12]. In moor imagery, he subjec imagines raising heir righ/lef hand wihou any corresponding physical movemen [11], [2], and in P300 ERP a subjec s menal reacion o various visual simuli is measured [12]. Boh hese mehods are very useful in various medical applicaions [3], [2], however hey rely on EEG paerns ha could easily be replicaed during daily aciviy, which limis he operaional capabiliy of he user. For example, if lef/righ arm moor imagery is used o classify wo asks, any acion requiring he subjec o move heir arms while using he EEG- BCI device could resul in he sysem wrongly regisering he resuling EEG paern as a menal command. As such, hese sysems rely on he assumpion ha he EEG-BCI sysem will only be used while he user is no moving he body par ouside of performing he asks assigned o he device. In an effor o overcome his limiaion, a mehod for robo conrol using conscious menal asks ha do no originae from bodily movemen (i.e. moor imagery or ERP) or hough processes ha migh arise from daily aciviy (e.g. mah problems, word/leer composiion, ec.) is explored. These specific, recreae-able menal asks are referred o as absrac hough in auhors work and in his paper. As shown in previous work by he auhors, absrac hough classificaion for BCI based robo conrol is a difficul ask [1], [13], especially when using low cos consumer grade EEG devices for daa acquisiion. Consumer grade, commercial-offhe-shelf EEG devices are used in his work due o heir ease of use and ease of acquisiion. Though he consumer grade producs are cheap and easy o use, hey sacrifice he high accuracy of clinical models. Therefore, in order o make hese commercial devices pracically viable, highly robus and accurae algorihms are needed. Daa preprocessing and feaure selecion are exremely crucial seps in EEG-BCI sysems since EEG signals are shown o have low signal o noise raio and o be high dimensional [4], [13]. This paper focuses on he sep of feaure selecion. As shown in previous work by he auhors, absrac hough classificaion for BCI based robo conrol is a difficul ask [1], [13], especially when using low cos consumer grade EEG

2 devices for daa acquisiion. Consumer grade, commercial-offhe-shelf EEG devices are used in his work due o heir ease of use and ease of acquisiion. Though he consumer grade producs are cheap and easy o use, hey sacrifice he high accuracy of clinical models. Therefore, in order o make hese commercial devices pracically viable, highly robus and accurae algorihms are needed. Daa preprocessing and feaure selecion are exremely crucial seps in EEG-BCI sysems since EEG signals are shown o have low signal o noise raio and o be high dimensional [4], [13]. This paper focuses on he sep of feaure selecion. Feaure selecion is he process of finding he mos discriminaing properies of a daa se, while keeping he oal loss of informaion as low as possible [14]. Various mehods of achieving his have been discussed in lieraure and i has been shown ha feaure selecion can suppor he performance of EEG-BCI sysems. In [15], [16] and [17] he auhors use Geneic Algorihms for finding he opimal feaure se from EEG daa. Common Spaial Paerns were used as feaures in [18] and [19] in order o find he maximum variance in signals beween muliple classes. In [20] and [21], exraced parameers via Auoregressive (AR) modeling are used as feaures. Oher algorihms seen include Principal Componen Analysis (PCA) [22], Neuro-fuzzy opimal feaure selecion [23], signal processing mehods [24] and oher evoluionary mehods [25], [26]. In he ineres of breviy, all he mehods are no elaboraed upon, more deails can be found in [27]. The lieraure survey showed ha feaure selecion has he capabiliy of improving he performance of EEG-BCI applicaions. Therefore, his paper presens a mehodology for selecing he se of opimal feaures from EEG daa for improving he performance of absrac hough driven robo conrol. The presened mehodology views he feaure selecion process as an opimizaion problem and uses he well documened and proven global opimizaion capabiliies of Geneic Algorihms (GAs). GAs were used because i has been shown ha GAs can be used successfully in feaure selecion mehodologies in general and specifically in EEG-BCI applicaions as well [15]-[17]. During he evoluionary process of GAs, he srengh of each candidae soluion is calculaed using a classificaion algorihm. Once he feaures were idenified, final classificaion is carried ou using a differen classificaion algorihm. The presened mehod was implemened on an absrac hough EEG daa se acquired using a consumer grade EEG device. The daa se comprised of hree hough paers peraining o robo conrol: 1) Res, 2) Move Forward, and, 3) Turn Lef. The resuls of he presened mehod were compared agains EEG classificaion wihou feaure classificaion. Several classifiers were esed for performing he final classificaion. The res of he paper is organized as follows. Secion II provides an overview of he algorihms ha are being used for he presened feaure selecion mehodology. Secion III elaboraes he presened feaure selecion mehod. Secion IV presens he specifics of implemenaion for he presened mehod. Secion V presens he conduced experimens and he obained experimenal resuls and finally Secion VI concludes he paper. Geneic Algorihm 1: Iniialize he populaion wih random soluions (individuals) 2: Evaluae populaion (Calculae finess of each individual) 3: Repea unil erminaion crierion is me 3.1: Selec parens (Selecion) 3.2: Recombinaion pairs of parens for new offspring (Crossover) 3.3: Muae offspring (Muaion) 3.4: Evaluae new populaion Fig. 1 Pseudo-code of he Geneic Algorihm (GA). II. GENETIC ALGORITHMS This secion provides a brief inroducion on Geneic Algorihms (GAs). GAs are a ype of evoluionary algorihms. Evoluionary algorihms are inspired by Darwin s heory of evoluion [28]. GAs simulae biological evoluion and i has been shown ha hey can be used for global opimizaion [28]. In order o encode he opimizaion problem in an evoluionary form, a se of candidae soluions o he problem are encoded in individuals. The se of individuals is called a populaion, or generaion. For each generaion, he srengh of each individual in he populaion, called finess, is calculaed using a finess funcion. The finess is a measure of how well he candidae soluion encoded by he individual performs wih respec o he opimizaion problem. The GA aemps o maximize he finess of individuals in he populaion. The iniial populaion is generaed by randomly generaing individuals. Then, using he individuals of he curren generaion, he nex generaion is generaed by using evoluionary operaors. The evoluionary operaors are selecion, muaion, crossover/recombinaion and eliism. Selecion is he process of choosing individuals from he curren generaion ha will be used as parens o produce offspring for he nex generaion. Two main mehods are used in lieraure for selecion: ournamen selecion and roulee wheel selecion. Once he parens are seleced, crossover/recombinaion is used o generae new offspring. The mos used crossover mehods are one poin, wo poin and uniform crossover. The crossover operaion is imporan in he opimizaion process o avoid being suck a local opima. The muaion operaion is used o perform small random changes in he offspring o inroduce random variaions ino he nex populaion. Finally, he eliism operaion makes one or more copies of he individual wih he bes finess in he curren populaion and moves hem over o he nex generaion wihou any change. The purpose of eliism is o ensure ha he bes finess of he nex generaion is a leas as good as he bes finess of he curren generaion. This evoluionary process is carried ou for a predefined se of generaions or unil he bes finess of populaion reaches an equilibrium. The average finess of he populaion and he bes finess of he populaion is kep rack of hroughou he process. Then, he bes individual (he individual wih highes finess) in he final populaion is considered as he opimal soluion for he problem. Fig. 1 shows he pseudocode of a GA.

3 III. OPTIMAL FEATURE SELECTION FOR ABSTRACT THOUGHT BASED ROBOT CONTROL This secion elaboraes he presened mehodology of opimal feaure selecion for absrac hough based robo conrol. Fig. 2 shows he process of he presened mehod. A. Daa Acquisiion The firs sep of he presened mehod is daa acquisiion. EEG daa for T number of acions are acquired from a noninvasive EEG measuring device wih muliple sensors. Thus, he dimensionaliy (number of feaures) of he daase is governed by he number of sensors ha he device consiss of. Therefore, a daa record ha is acquired a ime, from an EEG device wih M number of sensors a daa record wih M feaures can be expressed as: { 1 2 M d = S, S,..., S } (1) where S is he value of he i h sensor a ime. i Daa acquired from he EEG device are he elecrical aciviy of he brain. I.e., volage value from each elecrode is acquired a a given imesamp ( S values in Eq. (1)). Feaures i can be creaed from he elecrical daa by performing differen preprocessing operaions such as ime-frequency analysis [13]. In he work presened in his paper, for he purpose of demonsraing he mehodology of feaure selecion, feaures are considered o be he raw volage values read in from he sensors. Therefore, using he presened mehodology, he opimal se of sensors ha resuls in he bes classificaion accuracy is seleced. I has o be noed ha he presened mehodology can be exended o selec feaures of differen ypes ha are acquired from differen signal processing echniques. B. Encoding he Problem For selecing he opimal subse of he M feaures, an opimizaion problem is solved. The global opimizaion capabiliies of Geneic Algorihms (GA) are used for solving he opimizaion problem. As menioned, each EEG daa record ha is acquired from a device wih M sensors conains M number of feaures. Once he daa are acquired, he daa are encoded o be applied o he GA. As menioned before, in GAs, candidae soluions are encoded as an individual and a populaion of individuals is creaed. For each generaion a new populaion is creaed. In he problem of finding he opimal subse for improved classificaion accuracy, a candidae soluion is some subse of sensors. Therefore, a candidae soluion for he problem can be expressed as, n f = f, f,..., f } (2) s { 1 2 n n where f is a subse of feaures wih n feaures where n M. s For he iniial generaion of he GA, candidae soluions are randomly generaed for differen values of n. For he purpose of encoding he soluions wih equal size, a fixed lengh binary sring is used for represening a soluion. The binary sring Fig. 2: Absrac hough driven robo conrol framework consiss of M bis, one bi assigned o each feaure. If a feaure is seleced in a paricular candidae soluion, he bi peraining o ha feaure will be se o 1 and 0 oherwise. For insance, where M is 10 and for a candidae soluion where sensors 1, 2 and 5 are seleced, he individual represening he candidae soluion will be wrien as The iniial populaion is creaed wih P number of randomly creaed individuals. C. Finess Calculaion As menioned in Secion II, in a geneic algorihm he goodness of each individual is evaluaed for each generaion. For each generaion, each individual is evaluaed o acquire he measure of goodness, or he finess. In he presened problem of finding opimal feaures for improved classificaion, he finess of an individual should be relaed o he classificaion accuracy ha can be achieved wih he seleced feaures. Therefore, in order o evaluae an individual, each individual, he EEG daa classificaion process is carried ou o obain he classificaion accuracies. I.e. he chosen classificaion algorihm uses daa only from he seleced feaures as inpus o perform he classificaion. Therefore, he evoluionary process in he GA will aemp a maximizing he classificaion accuracy hrough he opimizaion process. D. Evoluionary Process Once he iniial generaion is creaed and finess is calculaed for all he individuals, he evoluionary process is carried ou o solve he opimizaion problem. In he evoluionary process, a new generaion is creaed a each ieraion. Wih each ieraion, i aemps o increase he

4 average finess of he populaion and he finess of he bes individual of he populaion. The individuals for he new generaion is creaed by using he crossover/recombinaion operaor. In crossover, wo parens are combined o produce one or wo children. As menioned, he crossover operaion can be perform in several mehods. In his paper, uniform crossover is performed. In uniform crossover, he wo parens are mixed o produce wo children subjec o a crossover rae. I.e. if he crossover rae is 50%, one child is creaed wih 50% random bis from paren 1 and 50% random bis from paren 2. The unused bis from he wo parens are used o make up he oher child. The parens are seleced for recombinaion using he selecion operaion. There are several mehods exising in lieraure o perform he selecion. This paper uilizes he ournamen selecion mehodology. In ournamen selecion, a predefined number of individuals, called he ournamen size, are picked a random. The winner of he ournamen is he individual wih he highes finess ou of he picked individuals. Tournamen selecion process is carried ou wice o selec he wo parens for one crossover operaion. Once he new generaion is populaed wih children, random muaions are inroduced o he children subjec o a muaion rae. This sep is carried ou o inroduce random variaions ino he new generaion. Muaion is implemened in his work by changing he values of random bis in randomly seleced children. In addiion o selecion, crossover and muaion operaions, he evoluionary process includes he eliism operaion as well. The eliism operaor reains a copy of he bes individual of he curren generaion in he new generaion. This operaion is carried ou o make sure ha he bes individual of he new generaion is a leas as good as he bes individual of he previous generaion. As menioned, he evoluionary process is carried ou for a predefined number of ieraions. Once he evoluionary process is complee, he bes individual of he final generaion represens he subse of feaures ha provides bes classificaion accuracy for he presened daase in he given number of ieraions. Therefore, he dimensions ha are seleced in he individual are he opimal feaures for classificaion. Therefore, daa from he seleced feaures are sen as he inpus o he final classifier. Even hough classificaion was performed for finess classificaion, anoher classificaion process is carried ou o acquire he final classificaion accuracy. Then, he EEG daa classificaion is performed by he classifier using only he seleced feaures. This sep is included in he mehod o demonsrae ha differen classificaion processes can be carried ou o improve classificaion accuracies afer idenifying he opimal dimensions. Theoreically, any classificaion algorihm can be used o perform he classificaion using he seleced feaures. IV. IMPLEMENTATION AND EXPERIMENTAL SETUP This secion elaboraes he implemenaion specifics of he presened mehod. This secion firs presens he implemenaion of he daa acquisiion mehodology and TABLE I. IMPLEMENTATION DETAILS OF THE GENETIC ALGORITHM Algorihm Populaion size Selecion mehod Eliism Crossover mehod Crossover rae Muaion mehod Muaion rae Generaional Geneic Algorihm 100 Tournamen Selecion wih a Tournamen size of 5 2 Copies of he bes individual reained Uniform Crossover 50% Randomly swapping values in dimensions wih a probabiliy of % elaboraes he daa se. Then, he secion deails he specifics of implemening he mehod presened in Secion III. A. Daa Acquisiion In his paper, EEG daa were acquired hrough he use of an Emoiv EPOC+ Neuroheadse, a low cos, commercial-off-heshelf, non-invasive, wireless EEG headse for BCI applicaions [6]. The EPOC+ was chosen due o is low cos, ease of use in a research seing, and because i has been shown o have excellen performance and reliabiliy when compared wih oher high grade research level equipmen [28 ], [30]. The Emoiv EPOC+ measures he users brain aciviy via 14 EEG channel sensors, plus wo CMS/DRL reference sensors, placed on he scalp. The sensors are posiioned according o he inernaional sysem [30] (see Fig 3). In order o provide guidance and visual simulus, a simple Graphical User Inerface (GUI) was creaed. The GUI was designed o be as simple as possible in order o minimize disracions for he subjec, as well as reduce any unwaned ocular arifacs. The GUI cycled hrough words/phrases which promped he subjec o perform he desired menal ask. Each subjec was sa in fron of a compuer running he GUI and was asked o perform each ask in he order ha hey appeared on he screen. Each ask was recorded for 20 seconds each, and he ask se was repeaed 5 imes per session for as many sessions as he user could complee. For purposes of proof-of-concep and demonsraion of he mehodology, daa acquired from a single user were used in he classificaion process. However, muliple daa collecion runs were carried ou for he same individual. The used individual was a male graduae suden. As menioned before, daa were acquired for hree absrac hough paerns: Res, Move forward, and Turn lef. The desired hough paern was recorded when he daa acquisiion was performed. The complee daase consised of daa records and 60% daa were used for raining and 40% for esing. The daa used in his sudy were he raw volage values acquired from he 14 elecrodes in he Emoiv EPOC+. I.e. he feaure selecion is carried ou on he raw volage values and no preprocessing echniques are applied on he daa. No preprocessing is carried ou because he emphasis in his work

5 TABLE II. CLASSIFCATION RESULTS GA base d FS NB ANN SVM No FS GA based FS No FS GA based FS No FS Accuracy Precision Res Precision Forward Precision Lef Fig. 3: Seleced Elecrodes by he Geneic Algorihms and heir placemen on he scalp according o he inernaional sysem is on demonsraing he imporance of feaure selecion. The inpu daa are he volage values and he oupu is he hough paern label which is recorded a he ime of daa acquisiion. B. Implemenaion of Opimal Feaure Selecion In implemening he mehod, daa from 14 sensors were used. Therefoare, number of sensors M in eq. (1) was se o 14. Daa were acquired for performing hree acions peraining o robo conrol. I.e. number T from Secion III was se o hree. Acions ha users were insruced o perform were Move forward, Turn lef and Res. These acions were chosen so ha i could be direcly ranslaed o robo conrol. In order o perform he evoluionary feaure selecion, a generaional GA was implemened. As menioned in Secion III, he GA was implemened wih individuals encoding candidae soluions in a binary sring. The lengh of he binary sring was equal o he number of dimensions/ feaures in he daa. Therefore, he lengh of he binary sring was se o 14. A populaion was considered o have 100 individuals. The evoluionary operaions of eliism, selecion, muaion and crossover were implemened for he generaional GA. More deails abou he GA implemenaion is given in Table I. As menioned in Secion III, he finess of each individual is evaluaed a each ieraion from he classificaion resuls. The finess was calculaed using he following funcion. Accuracy+ F i = 4 Where, F i is he finess of he i h individual, Accuracy is he classificaion accuracy of he used classifier. PR is he Precision for he h acion/class. Accuracy of he classifier can be calculaed as, 3 = 1 PR (3) Correcly_ classified_ records Accuracy = 100% (4) All _ esing_ records TABLE III. SELECTED ELECTRODES FROM THE GA Seleced Elecors AF3, F3, FC5, T7, P7, O2, P8, FC6, F4, F8, AF4 Precision (PR) for a class (Class A) is he probabiliy of a insance being classified correcly as Class A, given ha i is classified as Class A. PR was included in he finess funcion o preven he classificaion from favoring a more prominen class o improve accuracy in a skewed daase. For a paricular class, PR can be calculaed as follows. Correcly_ classified_ as _ A PR = 100% (5) A All _ records_ classified_ as_ A In order o keep in he same order of magniude as he classificaion accuracy, he PRs were considered as a percenage in he finess funcion given in eq. (5). A each ieraion, o calculae he finess of each individual, he classificaion was performed using a Suppor Vecor Machine (SVM). A Radial Basis Funcion (RBF) kernel wih a penaly parameer 1.0 was used for he SVM. In order o exend he SVM o a muliclass problem, one-vs-one classificaion mehodology was used. In order o analyze he effeciveness of he presened mehodology across classifiers, he final classifiers were used for performing he final classificaion was invesigaed using hree classifiers as he final classifier. The classifiers seleced were Arificial Neural Neworks [31], SVMs and Naïve Bayes [32]. I has o be noed ha, even hough differen classifiers were experimened for performing he final classificaion, SVMs were used for finess calculaion in he feaure selecion process. V. EXPERIMENTAL RESULTS This secion presens he experimens ha were carried ou and he resuls obained from he experimens. The presened selecion mehodology was compared agains EEG classificaion wihou any dimensionaliy selecion. As he firs experimen, he presened GA based feaure selecion mehodology was compared o a classificaion wihou any feaure selecion. The comparison was made wih respec o classificaion accuracies and precision raes for all

6 he hree classes. In order o invesigae he effeciveness of feaure selecion across classifiers, he experimen was carried ou using hree classifiers. However, he classifier used for obaining he finess of individuals in he GA raining process was kep as SVM o keep he feaure selecion process idenical across classifiers. The purpose of he sudy was o invesigae he effeciveness of using feaure selecion in performing he hough paern classificaion for robo conrol. Therefore, daa were no preprocessed before classificaion. When sudying he effeciveness of he mehod, comparison beween he accuracies beween he presened mehod and he mehod feaure selecion is emphasized, raher han he accuracies hemselves. I.e. The emphasis is no on he fac ha accuracy values are usable for robo conrol or no, bu on he fac wheher he accuracies are improved by performing he GA based feaure selecion. Once he opimal feaure se is idenified, differen mehodologies can be carried ou o improve classificaion accuracies. One such mehodology was presened in auhors previous work [13]. Table II summarizes he resuls obained in he experimen. I can be seen ha he presened mehodology for feaure selecion ouperforms a mehodology wihou feaure selecion. Furher, he analysis across he hree classifiers showed ha he feaure selecion improves he classificaion resuls regardless of he classifier used. An improvemen of 10.2% or higher was shown across all he classifiers. Furher, i was shown ha he SVM performed he bes ou of he hree classifiers esed. Given more daa from differen users, his poenially can change. However, i was noiced ha he resuls were heavily skewed owards he class Res. Even hough he precision for Lef and Forward were 100%, i can be seen ha mos of hose records were misclassified as Res. Therefore, he accuracy for Res and he overall accuracy is low. When he daa for Res were acquired, he user wasn insruced o hink of any paricular absrac hough. I.e. any inconsisen hough paern ha were hough of by he user a he Res porion of he daa collecion was labeled as Res. Therefore, here is a high probabiliy ha daa for Res has no consisen paern affiliaed o i. Therefore, paerns similar o lef and Forward can be included in Res. Therefore, when performing daa acquisiion, i is imporan o assign a consisen hough paern o Res as well. Table III liss he elecrodes ha were seleced by he GA as he sensors ha yield he bes classificaion accuracy for he given daase of absrac hough paerns. Fig. 3 shows he elecrode placemen according o he inernaional sysem and disinguishes he placemen of he seleced sensors. As menioned before, he classificaion resuls are low even afer he feaure selecion process. I has been shown in auhor s previous work ha using absrac hough as opposed o mehods like moor imagery is a difficul ask. Therefore, he classificaion algorihms should be improved o improve he classificaion afer he opimal feaure selecion mehodology. As menioned, previous work of auhors for classificaion [13], can be used as one poenial mehod o achieve beer classificaion. In order o improve classificaion accuracies so ha i is viable for robo conrol, oher preprocessing echniques and augmenaion of classificaion algorihms should be carried ou alongside he feaure selecion. VI. CONCLUSIONS This paper presened an iniial sep of a EEG-BCI based robo conrol framework which uses absrac hough as opposed o even driven mehods such as moor imagery. Feaure selecion was he presened iniial sep in his paper. The feaure selecion process was viewed as an opimizaion problem. The presened mehod uilized he global opimizaion capabiliies of GAs o find he opimal feaures. The raw volage values acquired from he elecrodes were considered as inpu daa. Therefore, opimal feaures were he opimal elecrodes o use for he classificaion. The mehod was esed on an EEG daase comprising of hree hough paerns: Res, Forward and Lef. The presened feaure selecion mehodology based classificaion was compared o classificaion wihou any feaure selecion. Three differen classifiers were used for boh mehods. For every classifier, he presened mehod ouperformed he mehod wihou feaure selecion wih an improvemen of 10% or higher. However, i was noiced ha he classificaion accuracy for Res was significanly lower han he oher wo acions. I should be noed ha he presened mehodology uilized he raw signals from he elecrodes wihou any preprocessing. Hence all he classificaion accuracies are less han adequae. The purpose of he sudy was o invesigae he usefulness of he feaure selecion mehodology for he given applicaion. As fuure work, differen preprocessing echniques will be used for noise reducion and ime-frequency analysis of signals. Then he presened feaure selecion mehodology would be exended for idenifying he opimal se of noise reduced feaures. For example, if ime-frequency analysis is performed using mehods such as Fas Fourier analysis or Wavele ransform, his feaure selecion mehodology could be used o idenify he opimal frequency bands or wavele coefficiens ha resul in he highes classificaion accuracy. Furher, he mehod should be esed for daa acquired from more users for muliple runs o acquire more generalized resuls. REFERENCES [1] D. Wijayasekara, M. Manic, Human Machine Ineracion via Brain Aciviy Monioring, in Proc. IEEE In. Conf. on Human Sysem Ineracion (HSI), pp , June [2] P. M. Shende and V. S. Jabade, Lieraure review of brain compuer inerface (BCI) using Elecroencephalogram signal, in Proc. of Pervasive Compuing (ICPC), 2015 Inernaional Conference on, pp. 1-5, [3] S. N. Abdulkader, A. Aia, M. M. Mosafa, Brain compuer inerfacing: Applicaions and challenges, in Egypian Informaics Journal, vol. 16, no. 2, pp , July [4] A. Maerka, P. 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