Design of Support Vector Machines with Time Frequency Kernels for classification of EEG signals

Abstract

The paper presents a classification method for EEG signals using Support Vector Machines (SVM) with Time-Frequency Kernels. Because of the non-stationary nature, the EEG signals do not exhibit unique characteristics in the frequency domain. Therefore, Time- Frequency transformations have been suggested to extract the common features for a particular mental task performed by different subjects. The Short-Time-Fourier-Transform (STFT) and Wigner-Ville type of Time-Frequency Kernels have been chosen for transforming the input data space into the feature space. Experimental results show that SVM classifiers using such feature vectors are very effective for classification of the EEG signals. The data obtained from ten different subjects each performing three different mental tasks, have been used for testing this method. The major contribution of this paper is in testing the different Time-Frequency Kernels belonging to Cohen's class. A comparative assessment of the classification performance with the conventional Gaussian Kernels in Time as well as Frequency domain has been also performed.