Spatio-temporal deep learning for EEG-fNIRS brain computer interface.

In this paper the classification of motor imagery brain signals is addressed. The innovative idea is to use both temporal and spatial knowledge of the input data to increase the performance. Definitely, the electrode locations on the scalp is as important as the acquired temporal signals from every individual electrode. In order to incorporate this knowledge, a deep neural network is employed in this work. Both motor-imagery EEG and bi-modal EEG-fNIRS datasets were used for this purpose. The results are compared for different scenarios and using different methods. The achieved results are promising and imply that combining both temporal and spatial information of the brain signals could be really effective and increases the performance.