EEG classification based on visual stimuli via adversarial learning

In this work, we propose a dual path deep learning architecture for the application of visual brain decoding. The inputs to the proposed network are the electroencephalogram (EEG) signals which are evoked due to the external stimuli, specifically, images in this case. The objective is to classify the EEG signals based on the image categories under which they were evoked. Our approach involves the combinations of convolution neural networks (CNN) on the time axis and the channel axis. Importantly, for the purpose of learning subject-invariant features, we also make use of the gradient reversal layer (GRL). This addition to our network boosts the performance of our system. In addition, we also propose to use guided back-propagation for the selection of more informative EEG channels, and finally, with the reduced number of channels, we estimate the performance of the proposed network which is almost similar to the version when considering all EEG channels.