Comparison of SVM and ANN for classification of eye events in EEG

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1 J. Bomedcal Scence and Engneerng, 2011, 4, do: /jbse Publshed Onlne January 2011 ( Comparson of SVM and ANN for classfcaton of eye events n EEG Rajesh Sngla 1, Brjl Chambayl 1, Arun Khosla 2, Jayashree Santosh 3 1 Department of Instrumentaton and Control Engneerng Natonal Insttute of Technology, Jalandhar, Inda; 2 Department of Electroncs and Communcaton Engneerng Natonal Insttute of Technology, Jalandhar, Inda; 3 Computer Servces Centre, IIT, New Delh, Inda. Emal: brjl.chambayl@gmal.com, rksngla1975@gmal.com, khoslaak@ntj.ac.n, jayashree@cc.td.ac.n Receved 11 November 2010; revsed 17 November 2010; accepted 19 November ABSTRACT The eye events (eye blnk, eyes close and eyes open) are usually consdered as bologcal artfacts n the electroencephalographc (EEG) sgnal. One can control hs or her eye blnk by proper tranng and hence can be used as a control sgnal n Bran Computer Interface (BCI) applcatons. Support vector machnes (SVM) n recent years proved to be the best classfcaton tool. A comparson of SVM wth the Artfcal Neural Network (ANN) always provdes frutful results. A one-aganst-all SVM and a multlayer ANN s traned to detect the eye events. A comparson of both s made n ths paper. Keywords: ANN; BCI; EEG; Eye Event; Kurtoss; SVM 1. INTRODUCTION The electroencephalogram, or EEG, conssts of the electrcal actvty of relatvely large neuronal populatons that can be recorded from the scalp. In healthy adults, the ampltudes and frequences of such sgnals change from one state of a human to another, such as wakefulness and sleep. The characterstcs of the waves also change wth age. There are fve major bran waves dstngushed by ther dfferent frequency ranges. These frequency bands from low to hgh frequences respectvely are called delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ). The man artfacts n EEG can be dvded nto patent-related (physologcal) and system artfacts. The patent-related or nternal artfacts are body movement-related, EMG, ECG (and pulsaton), EOG, ballstocardogram and sweatng. The system artfacts are 50/60 Hz power supply nterference, mpedance fluctuatons, cable defects, electrcal nose from the electronc components and unbalanced mpedances of the electrodes. Eye events (eye blnk, eyes close and eyes open) are normally consdered as physologcal artfacts n the EEG. But f we consder n a BCI pont of vew, these sgnals, although artfacts, can be used as good control sgnals. Eye blnk sgnals can be used n BCI applcatons lke vrtual keyboard whle the eye close and eyes open sgnals can be used for foldng and openng electrc foldable hosptal beds. SVMs (Support Vector Machnes) are a useful technque for data classfcaton. The foundatons of Support Vector Machnes have been developed by Vapnk (1995) and are ganng popularty due to many attractve features, and promsng emprcal performance. The SVM belongs to a class of machne learnng algorthms that are based on lnear classfers and the kernel trck. The am of Support Vector classfcaton s to devse a computatonally effcent way of learnng good separatng hyperplanes n a hgh dmensonal feature space, where good hyperplanes are ones optmzng the generalzaton bounds, and computatonally effcent mean algorthms able to deal wth sample szes of the order of nstances [8]. 2. EYE EVENT CHARACTERISTICS The eye event sgnals ncludes: eye blnk, eyes close and eyes open. Eye blnks are typcally characterzed by peaks wth relatvely strong voltages. There s also certan varablty n the ampltude of the peaks of a specfc ndvdual, more varablty between dfferent subjects. Eye blnks can be classfed as short blnks f the duraton of blnk s less than 200 ms or long blnks f t s greater or equal to 200 ms. Eye blnks can be classfed nto three types: reflexve, voluntary and spontaneous. The eye blnk reflexve s the smplest response and does not requre the nvolvement of cortcal structures. In contrast, voluntary eye blnkng (.e. purposely blnkng due to predetermned condton) nvolves multple areas of the cerebral cortex Publshed Onlne January 2011 n ScRes.

2 L. Zhao et al. / J. Bomedcal Scence and Engneerng 4 (2011) as well as basal ganglon, bran stem and cerebella structures. Spontaneous eye blnks are those wth no external stmul specfed and they are assocated wth the psycho-physologcal state of the person Ampltude The eye related sgnals wll be predomnant n the frontal and prefrontal regons of the bran. In the prefrontal lobe, say FP1-F3 or FP2-F4 electrode pars, a downward peak n the negatve regon shows an eyes open event and a postve peak shows an eyes close event. Also the ampltude of these peaks wll be sgnfcantly hgher compared to the rhythmc bran actvty. An eye-blnk sgnal can be detected by ts postve and negatve peak occurrences occurrences as shown n Fgure Kurtoss The EEG sgnal s stochastc, and each set of samples s called realzatons or sample functons (x(t)). The expectance (µ) s the mean of the realzatons and s called frst-order central momentum. The second-order central momentum s the varance of the realzatons. The square root of the varance s the standard devaton (σ), whch measures the spread or dsperson around the mean of the realzatons [5]. The kurtoss, also called fourth-order central momentum, characterzes the relatve flatness or peakedness of the sgnal dstrbuton [5], and s defned n (1), whch was modfed to refer to a non-gaussan dstrbuton. 4 x t Kurtoss E (1) The kurtoss coeffcent of an event s sgnfcantly hgh when there s an eyes-open, eyes-close or an eye blnk. The other spurous sgnals generated by patent movement, event lke swtchng ON/OFF a plug etc have a small value for kurtoss coeffcent. Hence eye events can be detected by kurtoss coeffcent. 3. ARTIFICIAL NEURAL NETWORK Artfcal Neural Networks (ANN) s smplfed models of the bologcal nervous system and therefore has drawn ther motvaton from the knd of computng performed by a human bran. An ANN, n general, s a hghly nterconnected network of a large number of processng elements called neurons n an archtecture nspred by the bran. Neural networks learn by examples. They can therefore be traned wth known examples of a problem to acqure knowledge about t. Once approprately traned, the network can be put to effectve use of solvng unknown or untraned nstances of the problem. Multlayer feed-forward network archtecture s made Copyrght 2011 ScRes. (a) (b) (c) Fgure 1. Eye event sgnal, (a) Eye blnk sgnal, (b) Eyes close sgnal and (c) Eyes open sgnal.

3 64 L. Zhao et al. / J. Bomedcal Scence and Engneerng 4 (2011) up of multple layers: an nput layer, a number of hdden layers and an output layer. Neurons are the computng elements n each layer as n Fgure 2. The acceleraton or retardaton of the nput sgnals s modeled by the weghts. The weghted sum of the nputs to each neuron s passed through an actvaton functon to get the output of a neuron. In addton to the nputs there are also bases to each neuron. 4. SUPPORT VECTOR MACHINES The Support Vector Machne mplements the followng dea: It maps the nput vectors x nto the hgh-dmensonal feature space Z through some nonlnear mappng, chosen a pror. In ths space, an optmal separatng hyperplane s constructed [9]. SVM method s based on the prncple of VC dmenson from the statstcal learnng and the Structural Rsk Mnmzaton (SRM). For non-lnear classfcaton, a non-lnear functon dmenson feature space, whch constructs an optmal classfer w x b (2) 0 The optmal hyperplane not only correctly separates the two class data ponts, but also makes the margn (dstance of the closest pont to the hyperplane) maxmal. By applyng the Lagrange Transformaton, the optmal classfer functon s derved, sgn l, f x a y K x x b (3) 1 where x s the tranng sample egenvector, x s the recognzng sample egenvector, a s the Lagrange operator, K xx, x x s called kernel functon. Kernel functons provde a convenent method to obtan the hgh-dmenson features mapped from the data wthout computng the non-lnear transformaton [10]. The common kernel functons are lnear, quadratc, polynomal and radal bass functon (rbf) kernels (Table 1). The support vector machne s a powerful tool for bnary classfcaton, capable of generatng very fast classfer functons followng a tranng perod. There are several approaches to adoptng SVMs to classfcaton problems wth three or more classes: Multclass rankng SVMs, n whch one SVM decson functon attempts to classfy all classes. One-aganst-all classfcaton, n whch there s one bnary SVM for each class to separate members of that class from members of other classes. Par-wse classfcaton, n whch there s one bnary SVM for each par of classes to separate members of one class from members of the other. The one-aganst-all classfcaton s used n ths paper. The archtecture of SVM s shown n Fgure 3. Fgure 2. Archtecture of ANN (3:m:n:3 neurons). Copyrght 2011 ScRes.

4 L. Zhao et al. / J. Bomedcal Scence and Engneerng 4 (2011) Fgure 3. Archtecture of SVM (N s the number of support vectors). 5. SIGNAL ACQUISITION AND PROCESSING The EEG sgnal s acqured usng Bopac MP36 system. The Bopac dsposable vnyl electrodes (EL 503) are placed on the FP1 and F3 regon n the Internatonal electrode system. The reference electrode s placed on the earlobe. The lead set SS2L connects the electrode to the Channel 1 (CH-1) of the MP36 system whch s further connected to the computer va USB port as shown n Fgure 4 and Fgure 5. The CH-1 of the Bopac MP36 system s set up as Electroencephalogram (EEG), Hz mode. In ths mode the gan of the amplfer s Two hardware flters, a 0.5 Hz hgh pass flter and a 1 khz low pass flter, are used n ths confguraton. Also a dgtal low pass flter havng 66.5 Hz cut-off and a 0.5 Q rato s employed. Ths ensures the nose free pckng up of EEG sgnals from the scalp electrodes. The samplng frequency s set at 200 samples per second. In MATLAB the EEG data s dvded nto 1000 sample wndows (5s). The kurtoss coeffcent, maxmum ampltude and mnmum ampltude of each wndow sample are taken out. The eye blnk sgnals are characterzed by hgh value of kurtoss coeffcent, normally above the value 3. The data s arranged n excel fles as kurtoss coeffcent, maxmum ampltude and mnmum ampltude. These are consdered as nputs to the neural network. Wth the help of the event markers, early recorded, an output set s defned correspondng to each sample wndow. 6. PREPROCESSING OF DATA FOR TRAINING Th e SVM and ANN wll learn the best from the tranng Table 1. Kernel Functons used wth SVMs. Kernel Functon Equaton Lnear K xx Quadratc Polyno, x x Kx, x xx 1 2 mal Kx, x xx 1 q RBF 2 2 K x, x exp x x Fgure 4. Block dagram of data acquston system. Fgure 5. Subject performng eye events accordng to the nstructons. Copyrght 2011 ScRes.

5 66 L. Zhao et al. / J. Bomedcal Scence and Engneerng 4 (2011) f the nput data and output data fall n the range of [-1, 1]. Hence all the data avalable s pre-processed usng PREMNMX command n MATLAB to span n the range [-1, 1]. After pre-processng, the entre dataset s dvded nto two, one for tranng the neural network and the other for testng the neural network. The feature space s shown n Fgure 6 and sample of data set n Table TRAINING, VALIDATION AND TESTING OF NETWORKS The ANN s developed usng the neural network tranng tool (nntool) n MATLAB. The nput layer contans 3 neurons, one for kurtoss coeffcent, and another for maxmum ampltude n the sample wndow and another for mnmum ampltude n the sample wndow. The output layer has three neurons, one for eye blnk and another for eye close and another for eye open detecton. Once the nputs and outputs are fxed we can vary the number of hdden layers, number of neurons n the ndvdual hdden layers, bases to ndvdual neurons and the actvaton functon used n each layer. The actvaton functon s fxed as tangent-sgmod functon. After dozens of tranng and performance evaluatons a confguraton havng two hdden layers (30 neurons n the frst hdden layer and 15 neurons n the second hdden layer) s selected. After fxng the confguraton tranng essentally means adjustng the weght matrces n the network so that the output neurons wll be tuned to the target. The standard ANN supervsed tranng algorthm for error backpropagaton [7] conssts of two steps: the forward propagaton and the backpropagaton. The forward propagaton step s acheved by applyng a tranng pattern to the ANN, propagatng t through the network and obtanng the contnuous output value. Ths output value s compared to the desred value of the pattern, generatng an error value. The error value s backpropagated to adjust the synaptc weghts of the neurons, characterzng the backpropagaton step. The Cross Valdaton (CV) procedure [7], appled to the supervsed tranng of neural networks, evaluates the tranng and the learnng of the ANN. The CV s executed durng the ANN tranng at the end of a tranng epoch and requres two pattern sets: the tranng set and the valdaton set. All tranng and valdaton patterns are presented to evaluate the tranng error and learnng error of ANN for that epoch. The errors can be evaluated by the mean square error. If the tranng algorthm s convergng, the tranng error s fallng towards zero. Normally, the learnng error falls to the best generalzaton pont, and then contnuously ncreases, whch ndcates over-tranng and the Fgure 6. Eye events n feature space. Table 2. Inputs and outputs of SVM and ANN. Sl. No. Inputs Outputs Kurtoss Coeffcent Maxmum Ampltude Mnmum Ampltude Eye blnk Eyes Close Eyes Open Event Close Open Blnk Blnk Copyrght 2011 ScRes.

6 L. Zhao et al. / J. Bomedcal Scence and Engneerng 4 (2011) Fgure 7. Performance of ANN for eye blnk, eye close and eye open detecton. Fgure 8. Performance of SVM for eye blnk, eye close and eye open detecton. Copyrght 2011 ScRes.

7 68 L. Zhao et al. / J. Bomedcal Scence and Engneerng 4 (2011) loss of generalzaton. The testng of ANN s done by smulatng the ANN wth the testng set and then calculatng the error. Wth standard steepest descent, the learnng rate s held constant throughout tranng. The performance of the algorthm s very senstve to the proper settng of the learnng rate. If the learnng rate s set too hgh, the algorthm can oscllate and become unstable. If the learnng rate s too small, the algorthm takes too long to converge. It s not practcal to determne the optmal settng for the learnng rate before tranng, and, n fact, the optmal learnng rate changes durng the tranng process, as the algorthm moves across the performance surface. The tranlm s a network tranng functon that updates weght and bas values accordng to Levenberg-Marquardt optmzaton. The tranlm s often the fastest backpropagaton algorthm n the neural network toolbox, and s hghly recommended as a frst-choce supervsed algorthm, although t does requre more memory than other algorthms. The tranng of SVM s done by usng the svmtran functon n the MATLAB Bonformatcs toolbox. Durng tranng we can specfy the kernel functon to be used. Also many other parameters can be vared n the process of tranng. After tranng the functon returns a structure havng the detals of the SVM, lke the number of support vectors, alpha, bas etc. The data can be classfed usng the svmclassfy functon. Three SVMs are traned n one-aganst-all mode for eye blnk, eyes close and eyes open detecton. 8. RESULTS AND DISCUSSIONS A multclass one-aganst-all SVM and a Feed Forward Back Propagaton (FFBP) ANN are traned to classfy the eye events: eye blnk, eyes close and eyes open. The FFBP network s traned n just 23 seconds usng the tranlm algorthm and s faster than ANNs that uses other tranng algorthms. The network had obtaned a good performance (Mean Square Error, MSE) of about 10-8 at epoch 14 wth the best valdaton performance of at epoch 8. The network wth two hdden layers (3:30:15:3) proved to be better on the bass of classfcaton accuracy compared to other network confguratons tested. The above sad network had obtaned classfcaton accuraces of 89.3%, 88.3% and 82.8% for eye blnk, eye close and eye open respectvely. The overall classfcaton accuracy for ths network s 86.8% whch s good for an ANN. On the other hand, the SVM classfers are traned n a fracton of a second wth much better classfcaton accuraces. The ndvdual SVMs are traned wth dfferent kernel functons and ther classfcaton accuraces are calculated. Lnear, quadratc, polynomal and radal bass functon (rbf) kernels are used for tranng. In a multfuncton for class one-aganst-all strategy, a sngle kernel all the SVMs had not provded exctng results. So ndvdual SVMs are traned wth dfferent kernel functons and the ones wth the maxmum classfcaton accuraces are selected. For detectng the eye blnks from the other classes, the quadratc kernel SVM had got the maxmum classfcaton accuracy (91.9%). For the eyes close detecton also the quadratc kernel SVM had got the best classfcaton accuracy (86.5%). But for the eyes open detecton, lnear kernel classfer had got the maxmum classfer accuracy (94.0%). The rbf kernel SVM had also proved to be good classfers for eye event detecton. The performance of ANN and SVM s shown n Fgure 7 and Fgure 8 respectvely. So when the results of the SVMs and ANNs are compared the SVMs had got an overall classfcaton accuracy of 90.8% whle the ANN had got only 86.8% as shown n Table 3 and Table 4. Ths proves the superor performance of the SVM classfers over the ANN classfers for eye event detecton n EEG. 9. CONCLUSION Ths contrbuton presented a new applcaton of the SVM and ANN classfer to detect the eye events, the eye blnk, the eyes close and the eyes open, n the EEG sgnal. Kurtoss coeffcent, maxmum ampltude and Table 3. Comparson of varous kernel functons. Kernel Functon Classfcaton Accuracy Eye Blnk No. of Support Vectors Classfcaton Accuracy Eyes Close No. of Support Vectors Classfcaton Accuracy Eyes Open No. of Support Vectors Lnear 88.5% % % 50 Quadratc 91.9% 13 Polynomal (Order 3) 85.2% 12 Polynomal (Order 4) 86.9% 11 Radal Bass Functon 90.2% % % % % % % % % 95 Copyrght 2011 ScRes.

8 L. Zhao et al. / J. Bomedcal Scence and Engneerng 4 (2011) Table 4. Comparson of SVM and ANN. Maxmum Classfcaton Accuracy Obtaned for SVM ANN Confguraton 3:30:15:3 (neurons) 3:20:10:3 (neurons) Eye Blnk 91.9% 89.3% 71.8% Eyes Close 86.5% 88.4% 66.1% Eyes Open 94.0% 82.8% 84.7% Overall 90.8% 86.8% 74.2% mnmum ampltude n a sample wndow are successfully used to tran the networks to detect the eye event sgnals. The SVM provded a maxmum classfcaton accuracy of 90. 8%, whle the ANN provded only 86.8%. T hs proves that the SVM classfer have better performance than the ANN cl assfer. The classfers developed can be used for developng a BCI system that uses eye events as control sgnals, especally for locked-n patents lke those sufferng from Amyotrophc lateral scleross (ALS). REFERENCES [1] Sane, S. and Chambe rs, J.A. (2007) EEG sgnal proc- John Wley & S ons Ltd., Chchester. essng. [2] Ra jasekaran, S., Vjayalakshm Pa, G.A. (2008) Neural networks, fuzzy logc and genetc algorthms: synthess and applcatons. Prentce-Hall of Inda Prvate Lmted, New Delh. [3] Sngla, R. and Gupta, B. (2008) Bran ntated nteracton. Journal of Bomedcal Scence and Engneerng, 1, do: /jbse [4] Manolov, P. (2006) EEG eye-blnkng artfacts power spectrum analyss. Proceedngs of Internatonal Conference on Computer Systems and Technologes, Bulgara, June 2006, IIIA. 3-1-IIIA.3-5. [5] Soverzos k, M.A., Argoud, F.I.M. and de Azevedo, F.M. (2008) Identfyng eye blnks n EEG sgnal analyss. Proceedngs of the 5th Internatonal Conference on In- formaton Technology and Applcaton n Bomedcne, May 2008, Shenzhen, Chna, [6] Manolaks, D.G., Ingle, V.K. and Kogon, S.M. (2005) Statstcal and adaptve sgnal processng: Spectral estmaton, sgnal modelng, adaptve flterng and array processng. Artech House Publshers, London. [7] Haykn, S. (1998) Neural networks: A comprehensve foundaton. Prentce Hall, New Jersey. [8] Crstann, N. and Shawe-T aylor, J. (2000) An ntroducton to support vector machnes and other kernel-based learnng methods. Cambrdge Unversty Press, Cambrdge. [9] Vapnk, V. (1998) Statstcal Learnng Theory. John Wley & Sons, Chchester. [10] Xe, S.-Y., Wang, P.-W., Zhang, H.-J. and Zhao, H.-T. (2008) Research on the classfcaton of bran functon based on SVM. The 2nd Internatonal Conference on Bonformatcs and Bomedcal Engneerng, May 2008, Copyrght 2011 ScRes.