EEG classification using a simple CNN model for imagined and executed motor signals

A CNN model with a simple architecture intended for decoding imagined as well as executed electroencephalography (EEG) signals is proposed, which has demonstrated remarkable performance in two publicly available and renowned EEG datasets.