An Automatc Evaluaton System of the Results of the Thought- Operated Computer System Play Attenton usng Neural Network Technque MARIOS S. POULOS 1, ANDREAS G. KANDARAKIS, GEORGE S. TSINARELIS 3 1 Department of Archves and Lbrary Scences, Faculty of Prmary Educaton, 3 School Dffculty and Psychopathology 1 Ionan Unversty, Natonal and Kapodstran Unversty of Athens, 3 European Unversty Cyprus 1, Greece, 3 Cyprus 1 mpoulos@ono.gr, kaan1@ymal.com, 3 symbol44@otenet.gr Abstract: - Ths paper focuses on solvng the problems of preparng and normalzng data that are captured from a Play Attenton system, and are lnked wth sgnfcant relevant propertes. We adapt these data usng a Bayesan model that creates normalzaton condtons to a well ftted artfcal neural network. We separate the method n two stages: frst mplementng the data varable n a functonal mult-factoral normalzaton analyss usng a normalzng constant and then usng constructed vectors contanng normalzaton values n the learnng and testng stages of the selected learnng vector quantfer neural network.. Key-Words: - ADHD, Non-Drug Treatment, Play Attenton, Game-lke Vdeo Exercses, Learnng Throughout Lfe, Neural Network 1 Introducton 1.1 State of the Art: Attenton-defct/hyperactvty dsorder as defned by the Dagnostc and Statstcal Manual of Mental Dsorders, fourth edton, text revson (DSM-IV-TR; Amercan Psychatrc Assocaton, 000) s the term used to descrbe a specfc developmental dsorder. Chldren wth ADHD have sgnfcant defcences n sustaned attenton (nattenton), behavoral nhbton (mpulsvty), and the regulaton of actvty level (hyperactvty). The DSM-IV-TR ctes a prevalence of 3-7% of school age chldren as probably havng ADHD. About three tmes more boys than grls are dagnosed. ADHD usually becomes evdent n preschool or early elementary years, frequently persstng nto adolescence and adulthood [1]. Wlens et al. (00) ALSO found n ther study that 75% of ther preschool chldren and 80% of ther school-age sample had at least on other dsorder besdes ADHD, wth an average of 1, 4 addtonal dsorders []. Untl recently, ADHD has lacked a reasonably credble scentfc theory to explan ts basc nature and assocated symptoms, and to lnk t wth typcal developmental processes. Consequently, the vast majorty of research nto the treatment of ADHD has remaned exploratory or descrptve n nature. The treatment decsons have been guded by pragmatcs (whatever seems to work s retaned, and whatever doesn t s dscarded). Also the DSM crtera for ADHD have several lmtatons; an mportant one s developmental nsenstvty [3]. But now the feld of ADHD has reached a pont, where the neuropsychologcal, neuromagng studes are comng to set clear lmts about the orgns and the nature of ADHD. Neuropsychologcal constructs from the functons of the prefrontal-stratal network and ts nterconnectons wth other bran regons (such as the cerebellum) are related to the development of nhbton, self-regulaton, and executve functon [4]. So as bran scans reveal more about the bology of ADHD, scentsts are lookng also at non-drug therapes that address cogntve functons, especally memory. Cogntve neuroscentsts have long suspected that workng and long-term memory functons are major contrbutors to attenton and cogntve abltes [5], [6]. Scentfc research has demonstrated that the bran s adaptable and capable of learnng throughout lfe [7]. The bran has the ablty to reorganze tself by formng new neural connectons throughout lfe. Ths capacty of neurons and neural networks n the bran to change ther connectons and behavor n response to new nformaton, sensory stmulaton, development, damage, or dysfuncton s termed neuroplastcty. Due to the bran s capacty to form new neural pathways durng the learnng process, dsorders such us ADHD can be overcome, or at the very least, managed wth correct tranng [8]. Play ISBN: 978-1-61804-074-9 07
Attenton facltates ths reshapng for success n school and home. It allows the student to vew the attentve state n real-tme and to learn to ncrease focus and concentraton. Gradually, the student can retan the sklls and transfer them to the school and home. The Play Attenton method may prevent long-term problems by helpng the chldren to be less mpulsve and more Self - controlled [9]. Usng technology orgnally developed to help plots stay alert; Play Attenton s an ntegrated educatonal attenton tranng system employng a combnaton of branwave montorng, cogntve sklls exercses, and behavor shapng technques. It s a learnng system that uses H-Tec armband to read branwaves ndcatve of focus and concentraton and s a regstered trademark of Unque Logc & Technology, Inc. ADHD kds exhbt more theta waves (related to daydreamng) and less beta waves (related to thnkng actvtes) [10]. They show excessve slow branwave actvty (theta and alpha ranges) compared to non ADHD actvty. The slow branwave actvty ndcates a lack of control n the cortex of the bran. Play Attenton gathers and analyzes electroencephalogram (EEG) sgnals to provde audtory and/or vsual feedback. It does not strve to change branwave patterns (that s why s not a neuro or bo feedback) but to teach organzaton, mprove workng memory, tran chldren wth ADHD to complete tasks and become less dstracted. Sklls necessary for success at school, home, and work. Play Attenton utlzes Edufeedback TM technology. Edufeedback teaches the student to control hs or her attentve state n real tme. It conssts of a helmet smlar to the ones commonly used for bkng. The helmet pcks up the bran actvty n the form of EEG waves related to attenton. It s lned wth sensors that montor the chld s bran waves assocated wth the attentve state and cogntve processng. The game-lke vdeo exercses of Play Attenton run on almost any computer and the user control them by mnd alone. Canddates should be at least 6 years old and have IQ above 60. In order to gan long-term benefts from Play Attenton, the user must complete a mnmum of 40 h. tranng on the system. There are 5 games desgned to mprove dfferent aspects of attenton ncludng attenton stamna, vsual trackng and dscrmnaton of mportant vs. unmportant stmul, and short term memory processng. 1. Am and Scope Ths paper attempts to solve the problems of preparng and normalzng data lnked wth sgnfcant relevant propertes and captured from many sources by adaptng these factors to a frst phase usng a Bayesan model that creates normalzaton condtons wth an equtable dstrbuton between the sgnfcant relevant propertes of such types of data. It also ams to adapt ths normalzaton to a well-ftted neural network so t can accept pre-processng data for tranng and testng procedures. It does ths usng two separate procedures, frst mplementng the data varable n a functonal mult-factoral normalzaton analyss usng a normalzng constant and then usng constructed vectors contanng normalzaton values n the learnng and testng stages of the selected learnng vector quantfer neural network. For applcaton purposes of ths model we focus on the educatonal doman and more specfcally on automatc evaluaton system n order to construct a decson system able to learn to evaluate the qualty of a teachng process gven as nput a set of observatons descrbng the events that took place durng ths process. The requred responses of the system are boolean (yes or no) and form a statstcal model whch follows the t-test. The proposed method wll focus on the normalzaton of the nput data, enablng the effectve tranng of a neural network n order to provde evaluaton results for any gven set of data. The evaluaton of a teachng process can be any value from the fuzzy set: (0- Poor) Not at all or rarely, (1-nsuffcent) Occasonally, () Frequently and (3) Nearly all of the tme.. Schedule of Study Fgure 1 outlnes the schedule of ths approach. Followng our method, captured automatc evaluaton data are normalzed usng a Bayesan model and adapted to a well-ftted neural network. Fg. 1. The schedule of the proposed method s analyzed Problem Formulaton The orgnal model s based on Systematc Observaton [8], whch presents a process for categorzng Play Attenton observatons regardng events descrbng the teacher s and pupls ISBN: 978-1-61804-074-9 08
behavor n a well-constructed form. Ths method ntroduced a system whch has formed the bass for a number of studes n ths feld.. The propose method defnes teachng behavor as acts by the teacher whch occur n the context of classroom nteracton [1]..1 Proposed Formulaton The Play Attenton devsed a system whch s dvded nto 5 cases of games. Ths classfcaton s overvewed n Table 1. However, n order to apply our method we consder that the expected value for the number of occurrences of each game s equal to S/n, where S s the total number of observatons and n s the number of games. Furthermore, we consder a confdence probablty of a=0.05, rangng the area between x and x wth degree of freedom ν=5-1=4. 0.975, ν 0.05, ν Table 1. Play Attenton: Games and Educatonal Objectves Analyss n four (4) categores where T: Teacher, R: Response, I: Intaton, S s the total number of observatons and S (=1..5) s the number of observatons for each game Games Educatonal Objectves Groups 1 Attenton Stamna. Sustan attenton for a full fve mnutes wthout rest Vsual Trackng. Sustan attenton to a target whle the target s movng 3 Tme o Task. Begn a task quckly and mantan attenton to the task untl ts completon 4 Short-Term Memory Sequentng. Prolong attenton to objects and remember the sequence n whch they are dsplayed. Expected Result T-R Total=S Observaton Result S/10 Total=S S1/S T-R S/10 S/S T-R S/10 S3/S T-I S/10 S4/S Α=0.05 Accordng to prevous study [1], the proposed technque to formulate related features of nonstructural data s based on extractng related features from text data or documents by semantc analyss and formulates an event-specfc summary. But ths technque doesn t support our cases whch are based on % related results. In ths case, the soluton of ths converson could be found n the Bayes s theorem accordng to the posteror probablty measure s proportonal to the product of the pror probablty measure and the lkelhood functon. Proportonal to mples that one must multply or dvde by a normalzng constant to assgn measure 1 to the whole space,.e., to get a probablty measure. In a smple dscrete case we have P( D H0) P( H0) P( H0 D) = P( D) (1) where P(H 0 ) s the pror probablty that the hypothess s true; P(D H 0 ) s the condtonal probablty of the data gven that the hypothess s true, but gven that the data are known t s the lkelhood of the hypothess (or ts parameters) gven the data; P(H 0 D) s the posteror probablty that the hypothess s true gven the data. P(D) should be the probablty of producng the data, but on ts own s dffcult to calculate, so an alternatve way to descrbe ths relatonshp s as one of proportonalty: P( H 0 D) P( D H0) P( H 0) () Snce P(H D) s a probablty, the sum over all possble (mutually exclusve) hypotheses should be 1, leadng to the concluson that P( H D) 0 P( D H ) P( H ) 0 0 = P( D H ) P( H ) (3) In ths case, the recprocal of the value (4) 5 Dscrmnatory Processng. Only pay attenton to specfc targets whle excludng dstractons. T-I S/10 S5/S In our case accordng to Table 1 the pror probablty s gven by P(H 0 ) =S1/S. The frst condtonal probablty s gven by P(D H 0 ) = P(H 0 )/α*100. k(j)=1/p(d) (5) ISBN: 978-1-61804-074-9 09
s the normalzng constant [13] where j=1..9 s number of the parameters. Vector k(j) s of sze 1xj.. Neural network Archtecture We adopt as the deal neural network to accept an approprate ftted Bayesan vector k(j), snce the man applcatons of RBF have been shown to be n pattern wth a Bayesan classfer [14]. For ths purpose we select an RBF neural network classfer from a varety of avalable neural networks archtectures, for the followng reasons: The RBF network was preferred to a Multlayer Perceptron (MLP), because t can solve a gven problem usng fewer neurons and n sorter tme, yet wth the same success. The RBF network has the ablty to classfy ncomng vectors nto classes that are not lnearly separable because t uses statc Gaussan functon as the nonlnearty for the hdden layer processng elements [14]. The Gaussan functon responds only to a small regon of the nput space where the Gaussan s centered. Successful applcaton of ths classfer reles on fndng sutable centers for the Gaussan functons. An unsupervsed approach usually produces better results than supervsed learnng. Usng n classes accordng to the referred problem we desgn the node characterstcs and the RBF network topology. An RBF neural network can be consdered as a specal three-layered network. The nput nodes pass the nput values to the nternal nodes that formulate the hdden layer. The nonlnear responses of the hdden nodes are weghted n order to calculate the fnal outputs of network n the thrd (output) layer [14]. A typcal hdden node n an RBF network s characterzed by ts center, whch s a vector wth dmenson equal to the number of nputs to the node. The actvty νl(x) of the lth node s the Eucldean norm of the dfference between the nput vector k(j) and the node center and s gven by: v ( x) = x xˆ (6) The output functon of the node s a radally symmetrc functon. A typcal choce, whch s also used n ths work, s the Gaussan functon [14]: ν f ( ν ) = exp σ (7) where σ s the wdth of the node. 3 Ftted Model Accordng to Sectons.1,. and.3 we constructed the vector of the normalzaton data k(j). For the tranng procedure we constructed a satsfed number n vectors k(j), where n-h (h<n h E N) s the number of vectors used n the tranng procedure. It must be noted that the number of the vectors n s dstrbuted n the four target classes of the RBF neural network. These classes correspond to a decson Lker scale (excellent, good, satsfed, far). For mplementaton purposes we used 4 neurons and layers. In the testng procedure we used the sgmod functon and h vectors k(j). For the statstcal evaluaton of ths model we used t-test crteron n order to evaluate the sgnfcant probablty by consderng as hull hypothess that the system yelds a random results amng to the sgnfcant rejecton. 4 Concluson In ths paper we focus on a method solvng problems related to preparng and normalzng data lnked wth sgnfcant relevant propertes, whch have been captured from Play Attenton Evaluaton by adaptng these factors to a frst phase usng a Bayesan model that creates normalzaton condtons wth an equtable dstrbuton between the sgnfcantly relevant propertes of such types of data. Moreover, we descrbed how the normalzaton can ad the tranng of a well-ftted neural network. In the future, we would lke to perform an extensve statstcal evaluaton of our model wth Play attenton data obtaned through expermental questoners. Ths process can be supported by the software envronment proposed n [14], whch provdes a system adng the recordng of observed classroom events. Fnally, we would lke to extend [14] by enhancng t wth decson makng capabltes n order to help the teacher dentfy problems and provde formatve assessment. References: [1] Amercan Psychatrc Assocaton [ΑPA]. Dagnostc and statstcal manual of mental dsorders (4th ed., text rev.). Washngton, DC: (000). [] Wlens, T. E., Bederman, J., Brown, S., Tanguay, S., Monuteaux, M. C., Blake, C., et al. Psychatrc Comorbdty and ISBN: 978-1-61804-074-9 10
functonng n clncally-referred preschool chldren and school-age youth wth ADHD. Journal of the Amercan Academy of Chld and Adolescent Psychatry, 41, 6-68, (00). [3] Barkley, R. A. Attentondefct/hyperactvty dsorder. In E. J. Mash & R. A. Barkley (Eds.), Chld psychopathology (pp. 75-143). New York: Gulford, Press, (003). [4] Barkley, R. A. Attenton-defct hyperactvty dsorder. A handbook for dagnoss and treatment (3nd ed.). New York: Gulford Press, (006). [5] Rochstroh, S., & Scuwezer, K. The contrbutons of memory and attenton processes to cogntve abltes. Journal of General Psychology, 18, 30-4, (001). [6] Wall, J.T.; Xu, J.; Wang, X. "Human bran plastcty: an emergng vew of the multple substrates and mechansms that cause cortcal changes and related sensory dysfunctons after njures of sensory nputs from the body". Bran Research Revews (Elsever Scence B.V.) 39 (-3): 181 15, (00). [7] Kandaraks, A. G., Poulos, M. S. Teachng Implcatons of Informaton Processng Theory and Evaluaton Approach of Learnng Strateges usng LVQ Neural Network. WSEAS, Transactons on Advances n Engneerng Educaton, 3, 5, 111-119, (008). [8] Dragansk, B., Gaser, C., Kempermann, G., Kuhn, G., Wnkler, J., Büchel, C., and May, A. "Temporal and Spatal Dynamcs of Bran Structure Changes durng Extensve Learnng" The Journal of Neuroscence, 6(3):6314-6317, (006). [9] Pne, K., Nasrn, F. New treatment for Hyperactvty n chldren: Thought-operated computer system. Scence Daly. Retreved from http://www.scencedaly.com/ releases/ 010/01/ 100107083904, (January 18, 010). [10] [10]. Hammond, D. C., What s Neurofeedbach. Retreved from www.snr.org/uploads/whatsnfb.pdf, (006). [11] IATEFL, www.ttedsg.atefl.org/resources/artcles/6.d o (attached 9-3-010) [1] Yu, L., Wang, S., La, K. K.: An ntegrated data preparaton scheme for neural network data analyss, Knowledge and Data Engneerng, IEEE Transactons 18(), 17--30 (006) [13] Traven, H.G.C.: A neural-network approach to statstcal pattern classfcaton by semparametrc estmaton of a probablty densty functons. IEEE Trans. Neural Networks, 366--377 (1991) [14] Sarmves, H., Dogans, P., Alexandrds, A. et al.: A classfcaton technque based on radal bass functon neural networks. Advances n Engneerng Software 37(4), 18--1 (006) ISBN: 978-1-61804-074-9 11