Detection of alcoholic EEG signals based on whole brain connectivity and convolution neural networks

Abstract

Alcoholism is a common complex brain disorder caused by excessive drinking of alcohol and severely affected the basic function of the brain. This paper investigates classification of the alcoholic electroencephalogram (EEG) signals through whole brain connectivity analysis and deep learning methods. The whole brain connectivity analysis is proposed and implemented using mutual information algorithm. Continuous Wavelet transform was applied to extract time–frequency domain information in each selected frequency bands from EEG signal. The 2D and 3D convolutional neural networks (CNN) were used to classify the alcoholic subjects and health control subjects. UCI Alcoholic EEG dataset is employed to evaluate the proposed method, a 96.25 ± 3.11 % accuracy, 0.9806 ± 0.0163 F1-score result in 3D-CNN model was obtained via leaving-one out training method of all the testing subjects.