EEG Channel Estimation using CNN-Based Depression Classifier

Abstract

Depression is a mental disorder that results to deteriorating effects in the lives of many people around the world. Traditional methods of diagnosing depression are based on interviews and questionaries. However, there were studies that have shown the possibilities of detecting depression biomarkers and perform classification. Electroencephalogram (EEG) analysis is one way of doing this. A series of signal processing techniques were used to streamline EEG data into a form which are recognizable by a machine learning algorithm. In this study, a convolutional neural network (CNN) – based depression classifier was used to classify depression using three EEG systems with 5, 16, and 128 channel locations. The aim is to estimate if there are differences in terms of classification performance. Results show that a system with locations for 5 and 16 EEG channels can achieve 98% accuracy like a 128-channel system. Hence, EEG systems with fewer number of electrodes can be utilized for depression classification applications.