  The classification of different fine hand movements from EEG signals
represents a relevant research challenge, e.g., in brain-computer interface
applications for motor rehabilitation. Here, we analyzed two different datasets
where fine hand movements (touch, grasp, palmar and lateral grasp) were
performed in a self-paced modality. We trained and tested a newly proposed
convolutional neural network (CNN), and we compared its classification
performance into respect to two well-established machine learning models,
namely, a shrinked-LDA and a Random Forest. Compared to previous literature, we
took advantage of the knowledge of the neuroscience field, and we trained our
CNN model on the so-called Movement Related Cortical Potentials (MRCPs)s. They
are EEG amplitude modulations at low frequencies, i.e., (0.3, 3) Hz, that have
been proved to encode several properties of the movements, e.g., type of grasp,
force level and speed. We showed that CNN achieved good performance in both
datasets and they were similar or superior to the baseline models. Also,
compared to the baseline, our CNN requires a lighter and faster pre-processing
procedure, paving the way for its possible use in an online modality, e.g., for
many brain-computer interface applications.
