A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface

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 single individual by training its late layers with only 12-min individual-related data. The model tested with the 'EEG Motor Movement/Imagery Dataset' outperforms the current state-of-the-art models by achieving a accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of . The proposed methods could foster the development of future BCI applications relying on few-channel portable recording devices and individual-based training