Classification of the gaze fixations in the eye-brain-computer interface paradigm with a compact convolutional neural network

In attempt to improve the performance of a recently proposed hybrid human-machine interface, the eye-brain-computer interface (EBCI), we applied a compact convolutional neural network, the EEGNet, to short electroencephalogram (EEG) segments obtained during spontaneous and intentional gaze fixations, without the feature extraction step prior to classification. A statistically significant improvement of classification performance was obtained compared to with the results of the classifier previously used in the EBCI paradigm, which was based on shrinkage linear discriminant analysis (sLDA). Computation speed allows for using the EEGNet in the EBCI in online mode.