Single-Trial EEG Classification Using Logistic Regression Based on Ensemble Synchronization

In this paper, we propose an ensemble synchronization measure across all EEG channel pairs of a cluster based on Frobenius norm of the phase synchronization matrix, in a 0-1 scale enabling a direct comparison between clusters with different number of channels. Using this metric, we studied the intrahemispheric EEG synchronization in the lower gamma band (30-40 Hz) during 1229 single trials of an audio-visual integration cross modal task (CMT) recorded from five patients with schizophrenia and five healthy control subjects. Using ensemble synchronization measure and response latency of single trials recorded during the CMT as features for logistic regression, we could classify each single trial of EEG as belonging to a patient with schizophrenia or a healthy control subject with 73% accuracy, with an area under receiver operating characteristics curve of 0.83. We also propose a likelihood rating to denote the possibility of a subject belonging to the schizophrenia group.

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