Transfer learning (TL) has been widely used in electroencephalogram (EEG) based brain-computer interfaces . This paper proposes that TL could be considered in all three components (signal processing, feature engineering, and classification/regression blocks before sending out the control signal . 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. Especially, integrate data alignment can significantly . improve the . classification performance .

Links: PDF - Abstract

Code :

https://github.com/drwuHUST/TLBCI

Keywords : data - tl - signal - alignment - classification -

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