Neural, Fuzzy And Neurofuzzy Approach To Classification Of Normal AndAlcoholic Electroencephalograms

Ashkan Yazdani, Pedram Ataee, S. Kamaledin Setarehdan, Babak N. Araabi, Caro LucasControl and Intelligent Processing Centre ofExcellence, School ofECE, University ofTehran

Tehran, Iranemail. ashkanygngmail.com

epilepsy, body movement and/or movement imaginationABSTRACT and etc. which can be used for clinical diagnosis or brain

computer interfacing. Applications of EEG signalprocessing and analysis have been reported in areas such

According to the literature, many psychiatric as evaluating brain's dynamic models, diagnosis ofphenotypes, brain disorders andlor mental tasks can be anomalies like epilepsy [3], mapping these discovereddetected by analyzing EEG signals. One such a disorders to a special location in the brain [4],psychiatric phenotype is alcoholism. In this paper the classification of mental states [5], brain computerparameters of second order autoregressive model, peak interfacing [6] and designing of emulators for someamplitude ofthe power spectrum, mean ofabsolute value motor-sensory functions. EEG signal is usually acquiredand the variance ofthe signal are extracted as features of simultaneously from the electrodes which are located onthe signal. The dimension of the feature vector is then the head. The number and location of these electrodesreduced by means ofPCA. Next a method based onfuzzy determines the spatial resolution of the measurement. Ininference system as a fuzzy approach in classification is order to classify EEG signals, we usually utilize someinvestigated. In this methodfirst the data in each class is methods based on pattern recognition. We can see thedivided into two clusters separately and a Gaussian differentstagesofpatternrecognitionmethodsinfigure1.membership function is defined for each cluster.Classification is performed by means of if-then rules ju. figitiesgenerated in the previous step. Then an adaptive ocesx igneurofuzzy inference system is used for classification. EEC EEG SignalDue to the ability ofthe neurofuzzy inference system to be Acnqul n Processingtrained higher classification accuracy is achieved ext acrarnFinally with the use of a multilayer perceptron structure | a || tio|it is shown that an accuracy of 100% can be achievedfor I . z _ ~~~~~~~C[4 Labelseparating the two classes.

Output1. INTRODUCTION user

figurel. Different stages of EEG pattern recognitionAlcohol abuse causes many economical and social [7]

losses. Also hidden damages such as memory weakness,concentration and decision making impairments and etc. As it can be seen in figure 1, pattern recognition ofcan occur in consequence of permanent alcohol abuse. EEG signal includes signal acquisition, preprocessing,For example the effects of alcohol on evoked responses feature extraction and classification. We can use timehave been reported [1, 10]. It has also been shown that domain features (such as different parametric models likethese disorders and weaknesses will remain even for a AR parameters), frequency domain features (such asperiod of time after quitting alcohol [2].These Fourier transform coefficients), time-frequency domainimpairments can cause some serious accidents while features (such as STFT transform or wavelet transform)driving or operating machines where active presence and and nonlinear features in feature extraction stage. Forproper judgments are necessary. Therefore the classification statistical classifiers, artificial neuralavailability of quantitative reliable diagnostic methods networks, hidden markov models and etc. are usuallywhich can discriminate alcoholics and normal people used.sounds necessary. In this paper the parameters of second order AR model

Electroencephalogram is a signal that shows the is extracted from the signal and the power spectrum ofelectrical activity of the brain. This signal shows special the signal is estimated with this model. The peak of thepatterns during some psychiatric phenotypes such as spectrum is considered as the next feature and then the

102 Proceedings of the 5th International Symposium on image and Signal Processing and Analysis (2007)

ability of fuzzy, neurofuzzy and neural classifiers forclassification of alcoholics and normal subjects is 25studied.

20The rest of the paper is organized as follows. In

section 2 the problem under investigation in this paper 1,1will be introduced. In this part some explanations aboutthe EEC data used and the features extracted from the 10dataset will be given. In section 3 the classifier used inthis study will be introduced. Section 4 presents theresults of different methods and finally the discussion 0will follow in section 5. |

=5 9' )Lf

2. METHODOLOGY -10

0 50 160 150 200 250 300

2.1. The dataset

The EEG data used in this paper arose from a large (a)study to examine EEG correlates of geneticpredisposition to alcoholism. This study was performed 4at Neurodynamics Laboratory at the State University ofNew York Health Center at Brooklyn. There were twogroups of subjects: alcoholic and control. Each subject 2was exposed to either a single stimulus (S1) or to twostimuli (51 and S2) which were pictures of objectschosen from the 1980 Snodgrass and Vanderwart picture 1111 1 ITRlllI III[ fI1II 1II1IIIil 1PSset. When two stimuli were shown, they were presented 0 IIIin eitfromthe 1980 Snodgras anere SV was identical toS2 or in a non-matchedconditionwhere Swadifferede fromto -1S2. -2k;;alS:fg 1S [E f

We extracted a dataset from the archive whichcontains 1341 signals. 1129 of these signals are -3considered as train dataset and the other 212 signals areconsidered as test dataset. The train dataset is used in 0 I01oloo 200 a2 Itraining steps of the neurofuzzy structure and theartificial neural network which used as classifiers.

(b)2.2. Feature extraction

figure2. (a) Typical EEG data used in this study and

In fact the data used in this study is visual evoked (b) the Gamma band VEP signalpotential (VEP). Therefore first of all this potential We decided to extract the parameters of a second ordershould be extracted from the background EEG signal. ARMA model which is estimated with the use of burgThere are several methods based on averaging for algorithm. Then, with the aid of this model the powerextraction of VEP from EEG [8] but in this study we used spectrum of the VEP signal is estimated and the peak ofthe VEP signal in Gamma band. Normally the frequency the spectrum is considered as the next feature. Twospectrum of the EEG signal is limited to 30 Hz. simple statistical features which were the mean ofTherefore the signal in the Gamma band (3OHz-5OHz) absolute value and the variance of the signal of eachcan be considered as pure VEP. For extraction of the electrode are extracted as well. These features aresignal in Gamma band a bandpass FIR filter with zero extracted from the VEP signal of each 61 electrode andphase shift is utilized. The passband of this filter is set to together they are considered as a feature vector for eachbe from 30Hz to 50Hz. A typical example of the EEC subject.signal used in this study and the extracted Gamma band After this stage in the feature conditioning stage weVEP signal is illustrated in figure 2. normalized the feature matrix. Then the dimension of

Zhong and Ghosh [9] studied 20 measurements from feature vector is reduced to eight with the aid of principal2 subjects and managed to classify alcoholic and control component analysis (PCA) via singular valueclasses with an acceptable accuracy using the EEC data decomposition (SVD) ofthe covariance matrix.from one electrode as feature vector.

Proc. ISPA07 103

3. CLASSIFICATION APPROACHES The characteristics of the FIS classifier used in thisstudy are in table 1 and an overview of this system is

The basic principles of Fuzzy inference systems (FIS), shown in figure3.adaptive neurofuzzy inference systems (ANFIS) andmultilayer perceptron will be briefly discussedthroughout this section, whereas a detailed description _____can be found in [11I]. These classifiers were used for ip 4discriminating EEC features in two classes.

3.1. Fuzzy Inference System inp2 (4)

In order to be able to utilize this system for our inp3 (4)classification purpose first the data of each classI I(alcoholic and control) is clustered separately. Theclustering is performed by means of fuzzy C-means inp4 (4) %~gehb) f(u)clustering method and the data of each class is clusteredinto two clusters. Consequently we have four clusters so ip 4that the first and the second clusters contain alcoholicEEC signals and the third and fourth clusters contain 4rlsotu 2normal EEC signals. Then the mean and the variance ofthe data in each cluster are computed and by means ofthem a Gaussian membership function of each cluster is inp7 (4)computed for each dimension of the feature vector.

Since the dimension of the feature vector is reduced to ihp8 (4)eight by means of PCA, 32 membership functions areobtained which are used in generating if-then rules. The Systern ash: B inputs, I butpUtsi 4 rulesif-then rules are as follows. If feature of the first

Fgr3 h vriwo h I lsiirwt ihdimension belongs to cluster one/two and feature of the Fur3Thovviewut ofd thearFIocasifep wtheihsecond dimension belongs to cluster one/two and ... and ipt n iayotufeature of the eighth dimension belongs to cluster

Infgr4wecnsetem brhifutosone/two then the decision is that this signal is obtained winh figre 4eiewfercanuseerthe membrshiputnmefounctonfrom an alcoholic person. If feature of the first dimension wrmhich aedfinued aferclnunesterng oforoinpthnube foaur.belongs to cluster three/four and feature of the second Fro tifguewcaunrsandhowgoodtcntiuesteearblthiy fatrdimension belongs to cluster three/four and ... and i n o uhi otiue h eaaiiyfeature of the eighth dimension belongs to cluster _ lute_three/four then the decision is that this signal is obtained c1uet8qPr2 clui4tbetr4 dluster2from a normal subject The output of this system is binary 1 custer3and the value 1 shows that the decision of the system isthat the signal is obtained from an alcoholic while thevalue -1 implies that the system decided that this signal is 0.6obtained from a control subject. Therefore the fuzzyinference system used is sugeno type system which has -binary output.0.

CD~~~~~~~~~~~~Tablet. The fuzzy inference systemE

Type sugeno ~0AAnd Method product /Or Method max 02

Imp. Method product 7

3.2. Adaptive Neurofuzzy Inference System this study. If the output is 1 then it shows that thedecision of the network is that the signal is obtained from

This network is a sugeno type model which also has an alcoholic and the output -1 implies that the signal isthe ability of being trained. In order to present this model obtained from a control subject.inputs x and y are supposed to be networks inputs and if-then rules are as following: 3.3. Multilayer Perceptron

Rule 1: If (x is Al) and (y is Bi) then (fl =plx + qly + rl) In this part a multilayer perceptron network with onehidden layer is considered. The number of neurons in the

Rule 2: If (x is A2) and (y is B2) then (/2 = p2x + q2y + r2) hidden layer is considered five neurons and the numberof neurons in the output layer is set to be one. With this

Where A and B are two fuzzy sets. This system is structure the total number of weights which are to beshown in figure 5. trained are 45 whereas the number of train datapoints is

Layer Laver 2 L iy.r Ldr 4L: 1129. If the output is 0.9 then it shows that the decisionx 0of the network is that the signal is obtained from an

alcoholic and the output -0.9 implies that the signal isobtained from a control subject.t < <f~~~~~~~~

7 Pi t 4. RESULTS

At < WJ nifThe results of this paper and the correct classificationrates of the classifiers which are introduced in the lastsection can be found here.

First we will see the results of the fuzzy inferencesystem. After obtaining the membership functions for

Figure5. A typical two input ANFIS 1111 each dimension of the feature space four if-then rules areThis system has two kinds of nodes. The rectangles generated and since multiplication is defined for the And

represent the nodes that have some parameters and these method the value of each if-then rule is computed byparameters are to be trained with a learning algorithm. multiplication of the membership values of all 8The circles represent the nodes that are constant and dimensions. Then the decision would be the rule whichdon't have any parameters. has the greatest value of if-then rule. An overview of the

The first layer consists of adaptive neurons. These if-then rules in the fuzzy inference system is illustrated innodes calculate the amount of membership to each fuzzy figure 6.set. The parameters of these nodes are the parameters of In order to evaluate the function of classifiers, correctdifferent membership functions which are set to be classification rate, sensitivity and specificity are usedGaussian in this study. The second layer consists of which are defined as following.constant nodes which simply perform the logical And in Sensitivity is the ratio of positive decisions to all ofpremise part of the if-then rules. Multiplication is the positive cases and the specificity is the ratio ofconsidered as logical And in our model. The third layer negative decisions to all of the negative cases.consists of constant nodes which normalize the outputs of After performing the classification with the aid ofthe previous layer. The fourth layer adaptive neurons fuzzy inference system defined the correct classificationcompute a linear function of the outputs of previous layer rate of 94.18%, sensitivity of 93.37%0 and the specificityand the parameters of these functions are trained during of 94.64% are achieved for classification of the twothe training process. classes.

The learning method of this network is called Hybrid Then the adaptive neurofuzzy inference system is usedmethod. In this learning method first the parameters of as a classifier in order to learn the if-then rules needed forthe neurons in the first layer are considered constants the classification. In this part a variable step size is used.with some random initial values. Then the parameters of This classifier discriminates the two classes with theneurons in the fourth layer are trained with a LMS correct classification rate of 98.111%, the sensitivity of

* 96%~~~~~~~~~~~~~~~~A/anA the- spc f-ciyf 1 00%.-algorithm. In the next step these trained parameters are 90 n h pcfct f100considered as constant values and the parameters of At the end a multilayer perceptron network which isneurons of the first layer are train with an error defined in the last section iS used as classifier and the twobackpropagation algorithm. These steps are performed till classes become completely separate and correctthsopcodiio ocus classification rate, sensitivity and specificity of 10000 areThe inputs of this network are the 8 principal acivd

components which were the inputs of FIS classifier aswell and two fuzzy sets are supposed for each input. Anoutput neuron is also considered in the network used in

Proc. ISPAO7 105

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