The feature extraction method of EEG signals based on transition network

Abstract

High accuracy of epilepsy EEG automatic detection has important clinical research significance. The combination of nonlinear time series analysis and complex network theory made it possible to analyze time series by the statistical characteristics of complex network. In this paper, based on the transition network the feature extraction method of EEG signals was proposed. Based on the complex network, the epileptic EEG data were transformed into the transition network, and the variance of degree sequence was extracted as the feature to classify the epileptic EEG signals. Experimental results show that the single feature classification based on the extracted feature obtains classification accuracy up to 98.5%, which indicates that the classification accuracy of the single feature based on the transition network was very high.