A deep neural network-based transfer learning to enhance the performance and learning speed of BCI systems

Brain-computer interfaces (BCIs) suffer from a lack of classification accuracy when the number of electroencephalography (EEG) trials is low. This is therefore during the learning of a BCI for a subject, there is no clear protocol to use the captured knowledge of other trained BCIs. To overcome this, we have proposed a new parallel deep neural structure containing long short-term memory and multi-layer perception. Furthermore, a subject-to-subject transfer learning is exploited to improve both performance and learning speed. First, the proposed combinatorial classifier is trained over different subjects, then for each new case, a copy of this learned network is adopted to be fine-tuned by the EEG features of the new subject. The proposed method is assessed on an EEG dataset of motor imagery movements and compared to the support vector machines. The proposed method provides superior classification results and significantly speed up the learning process of the deep network.