Evaluation of EEG Connectivity Network Measures based Features in Schizophrenia Classification

Abstract

In this paper an architecture for the classification of Schizophrenia using EEG-based brain connectivity is proposed. Functional and effective networks were constructed from the EEG using a variety of connectivity measures and with graph theory metrics complex network features were extracted. Several classification algorithms were used for the evaluation of the architecture. Promising results were observed when using connectivity measures that also capture directionality properties of the network. The best classification accuracy was 82.36% and was achieved by Random Forest classifier with Direct Transfer Function as a connectivity measure.