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However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed patients and 121 normal controls) subjects, was constructed. The electroencephalogram (EEG) signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal\u2010lobe three\u2010electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal\u2010redundancy\u2010maximal\u2010relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods (Support Vector Machine, <jats:italic>K<\/jats:italic>\u2010Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. The classifiers\u2019 performances were evaluated using 10\u2010fold cross\u2010validation. The results showed that <jats:italic>K<\/jats:italic>\u2010Nearest Neighbor (KNN) had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. 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