Pattern classification and analysis of memory processing in depression using EEG signals

Abstract

An automatic, electroencephalogram (EEG) based approach of diagnosing depression with regard to memory processing is presented. EEG signals are extracted from 15 depressed subjects and 12 normal subjects during experimental tasks of reorder and rehearsal. After preprocessing noisy EEG signals, nine groups of mathematical features are extracted and classification with support vector machine (SVM) is conducted under a five-fold cross-validation, with accuracy of up to 70%-100%. The contribution of this paper lies in the analysis and visualization of the difference between depressed and control subjects in EEG signals.