A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface
Objective. Brain-computer interface (BCI) aims to establish communication paths between the brain
processes and external devices. Different methods have been used to extract human intentions from
electroencephalography (EEG) recordings. Those based on motor imagery (MI) seem to have a great
potential for future applications. These approaches rely on the extraction of EEG distinctive
patterns during imagined movements. Techniques able to extract patterns from raw signals represent
an important target for BCI as they do not need labor-intensive data pre-processing. Approach. We
propose a new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to
classify five brain states (four MI classes plus a ‘baseline’ class) using a data augmentation
algorithm and a limited number of EEG channels. In addition, we present a transfer learning method
used to extract critical features from the EEG group dataset and then to customize the model to the
sin...