Restoration of dry electrode EEG using deep convolutional neural network

Abstract

Electroencephalography(EEG) has been used widely in biomedical research and consumer products because of its reasonable size and cost. In order to reduce the electrical impedance between electrodes and skin of the scalp, we use conductive gel. However, it takes time to setup EEG. This problem is solved by dry electrodes, which do not require to use the conductive gel, however, the signal quality of dry electrodes is lower than that of wet electrodes. In this research, we propose a method to improve quality of the dry EEG signal. In order to design a restoration filter, we prepare wet and dry EEG signals recorded simultaneously. Then the filter is trained by both wet and dry EEG signals to restore wet EEG signal from dry EEG signal input. We used the fully connected deep neural network (DNN) and convolutional neural network (CNN). We conducted an experiment using the oddball paradigm to demonstrate the proposed method and compare with the classical Wiener filter.