A Modified Convolutional Neural Network for Resting-State EEG-Based Schizophrenia Classification with Weighted Electrodes

Abstract

Schizophrenia is a severe mental disorder that can result in hallucinations, delusions, and extremely disordered thinking and behavior. While electroencephalography (EEG) has been used as an auxiliary tool for diagnostic purposes in several recent studies, all EEG channels are treated homogeneously without addressing the dominance of certain channels. The main purpose of this study is to obtain the weight value of each channel as the quantitative representation of influence of each scalp area on the classification of schizophrenia phases, and then to apply the weight values to improve the accuracy of classification. We propose a new convolutional neural network (CNN) structure based on AlexNet to derive weight values as weight layer and classify the samples better. Our results show that the modified CNN structure achieves better performance in terms of time consumption and classification accuracy compared with the original classifier. Also, the visualization of the weight layer in our model indicates possible correlations between scalp areas and schizophrenia conditions, which may benefit future pathological study.