Joint Spatio-Temporal Filtering of Motion Imagery EEG Signals for Data Alignment in Transfer Learning

This paper introduces a novel joint spatio-temporal filtering algorithm and investigate its impact on data alignment in transfer learning (TL) for motion imagery (MI) tasks to deal with the variability in subjects, trials, or tasks. While spatial filtering is an integral part of the common spatial pattern (CSP) algorithm, temporal filtering is introduced to deal with EEG signals of each channel. The proposed algorithm jointly optimizes the coefficients and the subsequent alignment of covariance matrices. Experimental results on various public datasets validate the effectiveness of our proposed algorithm.