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Resting state electroencephalogram (EEG) abnormalities in clinicallyhigh-risk individuals (CHR), clinically stable first-episode patients withschizophrenia (FES), healthy controls (HC) suggest alterations in neuraloscillatory activity. However, few studies directly compare these anomaliesamong each types. Therefore, this study investigated whether theseelectrophysiological characteristics differentiate clinical populations fromone another, and from non-psychiatric controls. To address this question,resting EEG power and coherence were assessed in 40 clinically high-riskindividuals (CHR), 40 first-episode patients with schizophrenia (FES), and 40healthy controls (HC). These findings suggest that resting EEG can be asensitive measure for differentiating between clinical disorders.This paperproposes a novel data-driven supervised learning method to obtainidentification of the patients mental status in schizophrenia research.According to Marchenko-Pastur Law, the distribution of the eigenvalues of EEGdata is divided into signal subspace and noise subspace. A test statistic namedLES that embodies the characteristics of all eigenvalues is adopted. differentclassifier and different feature(LES test function) are selected forexperiments, we have shown that using von Neumann Entropy as LES test functioncombine with SVM classifier could obtain the best average classificationaccuracy during three classification among HC, FES and CHR of Schizophreniagroup with EEG signal. It is worth noting that the result of LES featureextraction with the highest classification accuracy is around 90% in twoclassification(HC compare with FES) and around 70% in three classification.Where the classification accuracy higher than 70% could be used to assistclinical diagnosis.

A Data Driven Approach for Resting-state EEG signal Classification of Schizophrenia with Control Participants using Random Matrix Theory