Deep Learning Architecture analysis for EEG-Based BCI Classification under Motor Execution

One of the studies of the active brain-computer interface (BCI) focuses on identifying movements from human neurophysiological signals to control external devices such as robotic arms. In the literature, EEG-based BCI is utilized to decode user's information to perform action or fill the gap from the brain to the arms in the case of illness. The purpose of this work is to understand, among the scientific literature, what the best Deep Learning (DL) architecture is for motor execution (ME) classification. Data from 105 people from the Physionet dataset and 15 subjects from the Upper Limb dataset were used. EEGnetv4, Deep4Net, and EEGITnet were used to classify EEG signals under ME for real-time BCI. The best results were achieved from the EEGNET trained without Common Spatial Pattern transformation, for both datasets.