Cerebral Palsy EEG signals Classification:

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1 Cerebral Palsy EEG sgnals Classfcaton: Facal Expressons and Thoughts for Drvng an Intellgent Wheelchar Brgda Monca Fara DETI/UA Dep. Electrónca, Telecomuncações e Informátca/UA and ETP/IPP Escola uperor de Tecloga da aúde do Porto / Insttuto Poltécnco do Porto Avero and Porto, Portugal Lus Paulo Res EEUM Escola de Engenhara da Unversdade do Mnho, Departamento de stemas de Informação and LIACC Laboratóro de Intelgênca Artfcal e Cênca de Computadores, Unv. do Porto Gumaraes and Porto Nu Lau DETI/UA Dep. de Electrónca, Telecomuncações e Informátca / Unversdade de Avero and IEETA Insttuto de Engenhara Electrónca e Telemátca de Avero Avero Abstract Bran Computer Interfaces (BCI) enables nteracton between users and hardware systems, through the recognton of branwave actvty. However, the current BCI systems stll have a very low accuracy on the recognton of facal expressons and thoughts. Ths makes t very dffcult to use these devces to enable safe and robust commands of complex devces such as an Intellgent Wheelchar. Ths paper presents an approach to expand the use of a bran computer nterface for drvng an ntellgent wheelchar by patents sufferng from cerebral palsy. The approach was based on approprate sgnal preprocessng based on Hjorth parameters, a forward approach for varable selecton and several data mnng algorthms for classfcaton such as nave Ba, neural networks and support vector machnes. Experments were performed usng 30 ndvduals sufferng from IV and V degrees of cerebral palsy on the Gross Motor Functon (GMF) measure. The results acheved showed that the preprocessng and varable selecton methods were effectve enablng to mprove the results of a commercal BCI product by 57%. Wth the developed system t was also possble for users to perform a crcut n a smulated envronment usng just facal expressons and thoughts. Keywords Bran Computer Interface; Cerebral Palsy; Intellgent Wheelchars; Facal Expressons; Thoughts. I. INTRODUCTION The studes of the electrcal sgnals produced by the bran are addressed both to the bran functons and to the status of the full body []. By applyng dgtal sgnal processng methods to the electroencephalogram (EEG) sgnals obtaned by the bran actvty t s possble, for example, to obtan patterns for dagss and treatment of bran dsorders. The begnnng of research n terms of number of electrcal sgnals emtted by the nerves of the muscles goes back to the nneteenth century wth Carlo Matteuc and Eml Du Bos Reymond [2]. Although the research n ths area never stopped, the frst experments of EEG on humans belong to Hans Berger n 929. Besdes all the nterestng research approaches he also found the correlaton between the mental actvtes and the changes n the EEG sgnals makng possble the creaton of new human-machne nteractons. The communcaton devces based on EEG are kwn today as Bran-Computer Interfaces (BCI), mostly based on the research developed n the seventes at the Unversty of Calforna, Los Angeles and consequently scentfc papers that marked the frst appearance of the Bran-Computer Interface research area n the lterature [3] [4]. Cerebral Palsy (CP) s the term used for a group of nprogressve dsorders of movement and posture caused by abrmal development of, or damage to, motor control centers of the bran. CP s caused by events before, durng or after brth [5]. The abrmaltes of muscle control that defne CP are often accompaned by other neurologcal and physcal abrmaltes [6]. These physcal constrants lmt the daly lfe n terms of ndependence and automy. Therefore t s necessary to develop assstve techloges to mnmze moblty problems and to overcome some restrctons of patents. Assstve Techloges are defned as any product, nstrument, equpment or adapted techlogy specally planned to mprove the functonal levels of the ndvdual wth defcency [7]. Wheelchars are examples of ths knd of products. The evoluton of wheelchars allows today havng more sophstcate equpment and nterfaces. The term ntellgent wheelchar s more common and n the scentfc communty more attenton s gven to these brands of nstruments. However most of the studes do t nclude experments wth real patents and the works are confned to the research labs. The work presented n ths paper combnes the kwledge dscovery process, more precsely the process of acqurng and selectng varables, the experments wth real patents and the ntegraton of a bran computer nterface that allows drvng an ntellgent wheelchar. The paper s organzed as follows. After ths ntroductory secton, the second secton descrbes the BCI concept and the state of art of applcatons made usng ntellgent wheelchars. The thrd secton brefly descrbes our ntellgent wheelchar project and our approach on applyng kwledge dscovery technques to mprove the use of facal

2 expressons and thoughts as nputs for drvng an ntellgent wheelchar. Experments and results are presented at secton four and fnally conclusons and future work are presented n the last secton. II. BRAIN COMPUTER INTERFACE A Bran Computer Interface (BCI) s a type of devce whch allows nteracton between users and computer systems, through the recognton of branwave actvty. Normally, bran computer nterfaces are used n medcal contexts, wth the objectves of augmentng cogntve and sensory-motor functons. BCIs can be classfed n dfferent categores [8]: Invasve/Non-Invasve - ths classfcaton refers to how the BCI s placed to obtan the bran actvty. Invasve and partally-nvasve BCIs requre medcal and surgcal nterventon, snce they are mplanted n the user's bran. Non-nvasve BCIs do t requre bran mplants. However, n-nvasve BCIs are less effectve when compared to nvasve BCIs, snce the obtanable sgnal of branwave actvty s weaker. Dependent/Independent f a BCI nvolves a certan level of motor control from the user t s called a dependent BCI. On the other hand f t s t necessary any motor control from the users t s called an ndependent BCI. ynchrous/asynchrous - the computer drves synchrous systems by gvng the user a cue to perform a certan mental acton and then recordng the user's EEG patterns n a fxed tme-wndow. Asynchrous systems are determned by the user and operate by passvely and contnuously montorng the user's EEG data and attemptng to classfy t n the moment. The next subsectons present a bref descrpton of the bologcal matter of neural actvty, the rhythmc that can be acqured and the technques for measurng the bran actvty. A. Neural Actvty The central nervous system (CN) s the part of the nervous system that ntegrates the nformaton whch s receved from all parts of the body and coordnates all the actvty []. Bascally the CN s made of the bran and spnal cord. It s composed of axons, dendrtes and cell bodes. An axon (or nerve fber) s usually long and thn. Typcally, t conducts electrcal mpulses away from the neuron's cell body. Dendrtes are rmally shorter, become thnner wth dstance and are branched projectons of a neuron that acts n order to conduct the electrochemcal stmulaton receved from other neural cells to the cell body of the neuron from whch the dendrtes project []. Axons are dfferent from dendrtes n several features, ncludng shape, length n whch dendrtes are restrcted to a small regon around the cell body whle axons can be much longer, and functon where dendrtes usually receve sgnals whle axons usually transmt them. Axons make contact wth other cells, usually other neurons but sometmes muscle or gland cells, at junctons called synapses. At a synapse, the membrane of the axon closely adjons the membrane of the target cell, and specal molecular structures serve to transmt electrcal or electrochemcal sgnals across the gap. In the human bran each nerve s connected to thousands of other nerves []. An EEG sgnal s the measurements of the actvty that flows durng the synaptc exctatons of the dendrtes of many neurons n the bran []. When the neurons of the bran are actvated the synaptc flow s produced n the dendrtes. Wth ths current t s generated a magnetc feld whch can be measured by an electromyogram or a secondary electrcal feld over the scalp measured by EEG systems []. nce the human head s composed of dfferent layers such as scalp, skull, bran and other knd of thn layers, the sgnal measured at the scalp s attenuated. For that reason and because of the nternal, external and system ses, recordng electrc measures usng the scalp electrodes are only vable n areas of large populatons of bouncng neurons. Then t s necessary to amplfy these sgnals n order to dsplay the nformaton [9]. The EEG sgnals can be recorded from electrodes that are place on the scalp of the human bran. In order to ensure the relablty of the studes n terms of reproducblty over tme and subjects t was mplemented the Internatonal 0-20 system [0]. B. Intellgent Wheelchars and Bran Computer Interfaces An Intellgent Wheelchar (IW) s a locomoton devce to assst a user havng some knd of physcal dsablty, where an artfcal control system augments or replaces the user control [][2]. The man objectve s to reduce or elmnate the user's task of havng to drve a motorzed wheelchar. Usually, an IW s controlled by a computer, has a set of sensors and apples technques derved from moble robotcs research n order to process the sensor nformaton and generate the motors commands. The dea of ntegratng bran computer nterfaces n ntellgent wheelchars was already present n several works n the lterature that explored dstnct approaches to ths subject. The wheelchar prototype developed by the LURCH project [3] uses a n-nvasve BCI that allows the user to drve the wheelchar. By usng a headset equpped wth a number of electrodes, the user can tran thought patterns that wll be assocated to a certan output acton. In spte of beng n a premature state of development, ths techlogy mght be of good use for medcal purposes, namely for severely dsabled ndvduals. The Maa project s a European project amng at the development of an electroencephalography-based brancomputer nterface for controllng an automous wheelchar [4]. The wheelchar control has automatc obstacle avodance and s also capable of followng walls. The user can control the wheelchar movement gvng commands such as "go back" or "go rght". Blatt et al. [5] proposed a slghtly dfferent approach enablng a user to drve an ntellgent wheelchar, usng a BCI. In ths work, nstead of performng hgh-level commands, the user should contnuously drve the wheelchar.

3 Ather project under development at the Natonal Unversty of ngapore conssts of an automous wheelchar controlled through a P300-based BCI [6]. The man lmtaton of ths project s that the wheelchar movements are lmted to predefned paths. The user selects a destnaton, and the wheelchar automatcally calculates the trajectory to the desred place. If an unexpected stuaton occurs, the wheelchar stops and wats for further commands. Unfortunately, some problems may arse whle tryng to use a BCI n a complex task such as controllng a wheelchar. Bran actvty vares greatly from ndvdual to ndvdual, and a person's bran actvty also changes substantally over tme [7]. These obstacles make t dffcult to develop systems that can easly understand the user ntentons, especally for long perods of tme. Also, long perods of tranng are necessary before a user can correctly use a BCI to control a specfc devce [7]. The group of patents sufferng from cerebral palsy s also very challengng. Although t s possble to make a classfcaton of the level of severty, people sufferng from cerebral palsy are very heterogeneous. However, most of the work and applcatons of bran computer nterfaces to drve wheelchars do t consder the specfctes of ths populaton whch needs ths knd of assstve techlogy. III. KNOWLEDGE DICOVERY AND EEG APPLICATION FOR DRIVING AN INTELLIGENT WHEELCHAIR A. IntellWheels Project The IntellWheels project conssts of an ntellgent wheelchar platform that may be easly adapted to any commercal wheelchar and ad any person wth specal moblty needs [8] [9]. The frst prototype developed was focused on the development of the modules that provded the nterface wth the motorzed wheelchar electroncs usng a portable computer and other sensors. everal dfferent modules have been developed n order to allow dfferent ways of conveyng commands to the ntellgent wheelchar. A multmodal nterface was developed to drve the IW as can be observed n Fgure. There are several nputs that allow ths such as voce commands, joystck, buttons and head movements [20]. It s also possble to combne all the nputs, for example t s possble to push a button and say go for the IW follow rght wall. Recently t was ntegrated a bran computer nterface (Emotv ystem [2]) enablng to drve the IW also wth facal expressons and thoughts. Regardng the use of facal expressons to control the IW, from the multmodal nterface perspectve, only the expresson dentfed s acqured, along wth the system uptme, but n order to connect the BCI to the multmodal nterface applcaton, an addtonal applcaton was necessary [22]. On ths brdge applcaton t s possble to collect data from several varables. Ths brdge applcaton connects to the multmodal nterface as a clent and sends the recognzed facal expressons to be used as nputs n the multmodal nterface. Ths allows assocatng a hgh-level order to an expresson or sequence of expressons on the multmodal nterface. The brdge applcaton accepts the facal expresson recognzed as vald, f durng a defned perod of tme the expresson s detected a certan amount of tme wth a recognton percentage that exceeds a defned threshold. Fgure. Intellgent Wheelchar Multmodal nterface. However, the accuracy for dentfyng expressons and thoughts s very low wth users sufferng from cerebral palsy. For that reason t was acqured the raw data and several technques were appled such as preprocessng technques and varable selecton and data mnng n order to produce a better model for classfcaton. Besdes constructng the real prototype, a smulator was also developed n the context of IntellWheels project [20]. All the characterstcs and motons of the real IW could be performed n a very smlar way n the smulated envronment. On the smulator, an ntellgent wheelchar was modeled ncludng exactly the same sensors as the real wheelchar. Ths enabled to use the multmodal nterface to control both the real and smulated wheelchar just changng a smple confguraton parameter. Fgure 2 shows the real and the smulated prototype of the ntellgent wheelchar. Fgure 2. Real prototype (a) and smulated model (b) of the ntellgent wheelchar. The mportance of the smulator s huge, snce t enables to test algorthms and methodologes n a safe and low cost manner. B. Approach for applyng EEG sgnals One of the man objectves of usng EEG sgnals may be to facltate the communcaton between a machne and

4 people wth severe lmtatons. The process of EEG based control should follow several phases to gve the commands to the controller. The phases can be ntegrated n the process of kwledge dscovery. ) EEG data acquston and data selecton The data acquston was made usng a bran computer nterface avalable for research edton: Emotv ystem [2] [23]. The headsets are wreless and use a propretary UB dongle to communcate usng the 2.4GHz band. The headsets contan a rechargeable 2 hour lthum battery, 4 EEG salne sensors and a gyroscope. The oftware Development Kt (DK) used was the Research DK Edton. The descrpton of the technques appled on ths commercal verson for recognzng thoughts and facal expressons are t avalable. Only a general descrpton of the methods can be found n the manual [23] and explanatons n the offcal forum [2]. However the raw EEG data from the devce can be acqure and analyzed enablng researchers to develop ther own facal expressons/thoughts recognton methods. The data receved by the Emotv headset bascally comes from 4 EEG channels and from the values of the gyroscope. In Fg. 3 t s possble to verfy n the 0-20 Internatonal ystem the EEG channels that are ncluded n Emotv headset (sgnaled n grey). raw data by some of the facal expressons acqured from the patents. Fgure 4. Examples of the values of the EEG channels by facal expresson. It s clear a dfferent pattern of the EEG values durng the tme of each facal expresson. To extract the fnal features for classfcaton t was necessary to perform preprocessng. Fgure 3. EEG channels avalable n the Emotv [Adapted from [23]]. The data selected to be analyzed are the raw data from each sensor: AF3; F7; F3; FC5; T7; P7; O; O2; P8; T8; FC6; F4; F8; AF4, the values from the gyroscope and the tmestamp. The EEG sgnal unts are mcrovolts and the samplng rate s 28 Hz. There s also a partal samplng between seconds n whch s gven nformaton about all the varables. The varables GYROX and GYROY are horzontal and vertcal acceleratons from the gyroscope. Ths data refers to several facal expressons and thoughts. The ntal set of facal expressons s: smle; left smrk; rght smrk; blnk the e; blnk the left eye; blnk the rght eye; furrow; clench; eyebrows and rmal. The possble thoughts asked are: forward; back; left; rght; left spn and rght spn. Fg. 4 shows graphcal examples of the 2) Preprocessng everal steps were performed n ths phase of preprocessng. Fg. 5 shows a general overvew of these steps. Fgure 5. Overvew of the preprocessng stages.

5 The frst step n preprocessng was to apply a mean flter to the partal samplng of the EEG sensors as n () and Gyroscope as n (2): k j x j = EEG EEG () k x = k j j = Gyr Gyr k = where k = 0,,n partal samplng and j s the tmestamp that correspond to a facal expresson or thought. It was necessary to check f the expressons were performed correctly. Next, n order to extract the features, the Hjorth parameters [24] for each expresson and thought were calculated. The Hjorth parameters [25] present three measures to characterze the EEG sgnals n terms of ampltude, tme scale and complexty [26]. These parameters can dscrmnate between mental states. The parameters are: Actvty (Ac) t s a measure of the mean power of the sgnal. It s measure usng the standard devaton of the sgnal: l Ac = ( ) (3) l = where l s the ampltude of tme correspondng to the facal expresson or thought; =, 6 and s the sgnal. Moblty (Mo) t represents the mean frequency n the sgnal. Ths measure can be calculated as the rato of the standard devaton of the slope ( d ) and the standard devaton of the sgnal as n (3) d Mo = (4) Ac Complexty (Co) the objectve wth ths measure t s to capture the devaton from the sne wave (the softest possble curve). It s expressed as the number of the standard slopes actually seen n the sgnal durng the average tme requred for one standard ampltude devaton. Equaton 5 allows calculatng the complexty: dd Co = (5) where dd s the standard devaton of the second dervatve of the EEG sgnal. At ths stage the dataset as composed of the Hjorth Parameters for all EEG and gyroscope values as features and the class composed of all vald expressons and thoughts. After the frst experments t was also necessary to apply an optmzed selecton of varables elmnatng rrelevant and redundant features. Fg. 6 shows an example of the varaton of the Hjorth parameters and the dfferences between each facal expresson usng the error bars. Ac (2) Fgure 6. Vsualzaton of dfferent patterns of facal expressons usng the Hjorth parameters. Therefore the procedure of optmzed selecton of varables performs feature selecton algorthm wth forward selecton. The mplementaton was developed usng RapdMner [27]. The forward selecton s characterzed for startng wth an empty selecton of attrbutes and, n each round, t adds every one unused attrbute of the gven set of examples. For each added attrbute, the performance s estmated. Only the attrbute gvng the hghest ncrease of performance s added to the selecton. Then a new round s started wth the modfed selecton.

6 Fnally t was tested wth 0-fold cross-valdaton the best performance of models acheved by the technques of data mnng for multclass classfcaton. IV. EXPERIMENT AND REULT An applcaton was mplemented n order to record the raw data from the sensors as can be observed n Fg. 7. The methodology conssted n askng to users wth cerebral palsy to mmc several facal expressons. An occupatonal therapst was nvolved n the process and responsble for verfyng f the facal expressons were performed correctly. Ths nformaton was added to the data and only the correct expressons were added to the fnal data set. Experence, Automy, Independence and Constrants Varables n Varables n Vsual constrants Automy usng wheelchar Independence usng wheelchar 23 7 Audtve constrants The data set for constructng the classfcaton model has fve facal expressons types. In fact, the ndvduals could t correctly perform the left and rght smrks. The number of samples of furrow, left blnk and rght blnk was very low and for that reason were also elmnated from the dataset. The number of examples by each facal expresson s: 20 blnk; 20 clench; 4 eyebrows; 23 smles and 30 wth the natural facal expresson. In terms of thoughts all 30 examples by each though were consdered. The experments were dvded n two parts: the model for the facal expressons and the model for the thoughts. In each task t also performed the analyss of the accuracy wth and wthout preprocessng. The data mnng algorthms appled for comparson were: Naïve Ba; upport Vector Machnes, Neural Networks, Nearest Neghbour and Lnear Dscrmnant Analyss. The evaluaton was made usng the 0-fold cross valdaton. Table II presents the results acheved wthout optmzed selecton of varables TABLE II. ACCURACY AND PARAMETER FOR FACIAL EXPREION AND THOUGHT WITHOUT ELECTION OF VARIABLE Fgure 7. Manager to log the facal expressons and thoughts The ndvduals ncluded n ths study suffer from cerebral palsy and were classfed n the levels IV (23%) and V (77%) of the Gross Motor Functon Measure [28]. These are the hghest levels n the cerebral palsy severty degree. The sample sze was composed of the 30 ndvduals and all requre the use of a wheelchar. The mean of age was 28 years old wth 73% males and 27% females. In terms of school level 36% just have the elementary school, 27% have the mddle school, 27% have the hgh school and only 0% have a Bc. The domnant hand was dvded as: 50% for left, 33% for rght hand and 7% dd t answer. The aspects related to experence of usng manual and electrc wheelchar were also questoned. Table I shows the dstrbuton of answers about automy and ndependency usng the wheelchar and constrants presented by these ndvduals. TABLE I. EXPERIENCE UING WHEELCHAIR, AUTONOMY, INDEPENDENCE AND CONTRAINT OF THE CEREBRAL PALY UER Experence, Automy, Independence and Constrants Varables n Varables n Use manual wheelchar Cogntve constrants Use electrc wheelchar 22 8 Motor constrants 00 0 Accuracy of models wthout optmzed selecton of varables Acc (%) Acc (%) Algorthms Expressons Thoughts Parameters Naïve Ba 36,36 5,79 laplace correcton upport Machnes Vector 28,73 5,70 Neural Netwoks 35,27 8,39 k-nearest Neghbour Lnear Dscrmnat Analyss 7,64 8,68 kernel type=radal bass functon; epslon=0.00 tranng cycles=500; learnng rate = 0.3; momentum=0.2 k=4; mxed measure=mxed eucldean dstance 25,9 23,9 --- The results are sgnfcantly better wth an optmzed selecton of varables, where an applcaton of neural network can acheve 55% of accuracy (Table III). The results of thoughts accuracy also ncrease, however are stll very low. TABLE III. ACCURACY AND PARAMETER FOR FACIAL EXPREION AND THOUGHT WITH PREPROCEING Accuracy of models wth preprocessng Acc (%) Acc (%) Parameters Algorthms Expressons Thoughts Naïve Ba 54,64 23,22 laplace correcton kernel type=radal upport Vector 43,55 26,96 bass functon; Machnes epslon=0.00

7 Accuracy of models wth preprocessng Algorthms Acc (%) Acc (%) Parameters Expressons Thoughts Neural Netwoks 55,36 27,57 tranng cycles=500; learnng rate = 0.3; k-nearest Neghbour Lnear Dscrmnat Analyss 55 25,56 momentum=0.2 k=4; mxed measure=mxed eucldean dstance 54 25, The fnal experments ncluded both the smulator and the shared control. It was modeled a smlar scenaro of the nsttuton where the patents used to be and t was tested the behavor of the IW wth the facal expressons and thoughts. Fgure 8. napshot of the cerebal palsy center modelled where the tests were performed (top) and three frst person vew snapshots (bottom). Ten trals were performed wth real users n order to test the system. The tests showed that t was possble to complete the crcut usng only facal expressons and thoughts as the wheelchar nputs. The mean tme for dong the crcut usng other knds of nput methods was 53 (wth a standard devaton of.4 seconds). Wth facal expressons the average tme was 5 42 wth a standard devaton of 9.2 seconds. Fnally usng only thoughts as the wheelchar nput method, the average tme necessary was 2 3 wth 7.7 seconds of standard devaton. Although t was possble to complete the crcut usng thoughts, ths nput method s stll far from beng comfortable to enable drvng n a robust manner, an ntellgent wheelchar. However, there are patents, n the group used for the tests, that are completely unable to use any other knd of method for drvng the wheelchar n a robust manner. Thus, ths method, although t comfortable, may stll be very useful. V. CONCLUION AND FUTURE WORK The populaton usng electrc or manual wheelchars s very hgh n the group of patents sufferng from cerebral palsy. The wheelchars are very mportant assstve techloges to automy and ndependence. However there s a group that necessty partcular adaptaton. In fact there are cases whch do t have cogntve defcts but severe physcal handcaps. Ths paper presents an approach that provdes a platform that could be adapted to any electrcal wheelchar and where t was ntegrated a multmodal nterface. The objectve of ths multmodal nterface s to gve the best choce to a user for drvng the wheelchar. nce there are patents wth severe physcal problems a bran computer nterface was ntegrated n the multmodal nterface. After the frst experments and as expected, the low accuracy of facal expresson and thoughts recognton was shown. However, usng the raw data provded by the EEG sensors, applyng approprate sgnal preprocessng based on Hjorth parameters, a forward approach for varable selecton and usng a neural network t was possble to verfy a substantal mprovement on the facal expressons recognton. Ths enabled to drve the wheelchar usng only facal expressons and thoughts. For future work more data s gong to be collected n order to provde better classfcaton models. An nterestng pont to be tested s concerned wth the adaptaton of the language for each user. Ths means that f a user only can perform a sngle facal expresson such as smlng or ths s the only expresson that the classfcaton model can accurate predct, the concept of sequence assocated to an acton s gong to be appled usng only the robustly detected expressons. For example, smlng once may be assocated wth gong forward and smlng twce may be assocated wth turnng rght. We beleve that ths wll enable even patents wth very severe cerebral palsy, Parknson dsease or even Moebus syndrome to drve our ntellgent wheelchar. ACKNOWLEDGMENT Ths work was funded by the ERDF European Regonal Development Fund through the COMPETE Programme (operatonal programme for compettveness) and by Natonal Funds through FCT - Portuguese Foundaton for cence and Techlogy wthn project «INTELLWHEEL - Intellgent Wheelchar wth Flexble Multmodal Interface, RIPD/ADA/09636/2009». The frst author would lke to ackwledge also FCT for the PhD cholarshp FCT/FRH/BD/4454/2008. REFERENCE []. ane and J. Chambers, EEG gnal Processng, England: John Wley & ons, Lda, [2] C. Zywetz, A Bref Hstory of Electrocardography - Progress through Techlogy, Hanver: Bosgna Insttute for Bosgnal Processng and ystems Research, [3] J. Vdal, Toward drect bran-computer communcaton, Annual revew of bophyscs and boengneerng, 973, pp [4] J. Vdal, Real-Tme Detecton of Bran Events n EEG, IEEE Proceedngs, vol. 5,. 65, 977, pp [5] P. Rosenbaum and D. tewart, The world health organzaton nternatonal classfcaton of functonng, dsablty, and health: a model to gude clncal thnkng, practce and research n the feld of cerebral palsy, emnars n Pedatrc Neurology, vol.,., March 2004, pp. 5-0.

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