Lyapunov features based EEG signal classification by multi-class SVM

Electroencephalographms (EEGs) are records of brain electrical activity. It is an indispensable tool for diagnosing neurological diseases, such as epilepsy. Wavelet transform (WT) is an effective tool for analysis of non-stationary signal, such as EEGs. Wavelet analysis is used to decompose the EEG into delta, theta, alpha, beta, and gamma sub-bands. Lyapunov exponent is used to quantify the nonlinear chaotic dynamics of the signal.. Furthermore, the distinct states of brain activity had different chaotic dynamics quantified by nonlinear invariant measures such as Lyapunov exponents. The probabilistic neural network (PNN) and radial basis function neural network were tested and also their performance of classification rate was evaluated using benchmark dataset. Decision making was performed in two stages: feature extraction by computing the Lyapunov exponents, Wavelet Coefficients and classification using the classifiers trained on the extracted features. Our research demonstrated that the Lyapunov exponents and Wavelet Coefficients are the features which well represent the EEG signals and the multi-class SVM and PNN trained on these features achieved high classification accuracies such as 96% and 94%.