Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline

Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance. After electroencephalogram (EEG) signal acquisition, a closed-loop MI-based BCI system also includes signal processing, feature engineering, and classification blocks before sending out the control signal to an external device, whereas previous approaches only considered TL in one or two such components. This paper proposes that TL could be considered in all three components (signal processing, feature engineering, and classification) of MI-based BCIs. Furthermore, it is also very important to specifically add a data alignment component before signal processing to make the data from different subjects more consistent, and hence to facilitate subsequential TL. Offline calibration experiments on two MI datasets verified our proposal. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduce the calibration effort.