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A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface.
A new Brain Computer Interface (BCI) approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four Motor Imagery classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. The model tested with the 'EEG Motor Movement/Imagery Dataset' outperforms the current state-of-the-art models by achieving a 99.38% accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of 99.46%.