Fourier-Bessel series expansion based technique for automated classification of focal and non-focal EEG signals

In this paper, we propose a new method for automated classification of focal (epileptic) and non-focal (non-epileptic) electroencephalogram (EEG) signals. We use bivariate EEG signals of both epileptic and non-epileptic classes as our dataset. Difference time series of bivariate EEG signals is first computed to eliminate the effect of noise. Then the difference time series EEG signals are decomposed into coefficients using Fourier-Bessel (FB) series expansion. FB series expansion is a new method for signal decomposition that decomposes the signal into a finite and unique set of coefficients. The decomposition process yields coefficients which are further divided into 5 segments which are considered for the extraction of features, where for each signal 17 different features are computed. These extracted features are used for binary classification of EEG signals into epileptic and non-epileptic classes. We have implemented least square support vector machine (LS-SVM) along with various kernel functions such as linear, polynomial, and radial basis function (RBF) in our work. Classification accuracies obtained using these kernels and 10-fold crossvalidation are compared. With the proposed methodology, we can classify the EEG signals into focal and non-focal class with a significant accuracy.