Modified mixture of experts for analysis of EEG signals

Abstract

In this paper, the usage of diverse features in detecting variability of electroencephalogram (EEG) signals was presented. The classification accuracies of modified mixture of experts (MATE), which were trained on diverse features, were obtained. The wavelet coefficients and Lyapunov exponents of the EEG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the EEG signals, were then input into the implemented neural network models for training and testing purposes. The present study demonstrated that the MME trained on diverse features achieved high accuracy rates.