Transfer learning with data alignment and optimal transport for EEG based motor imagery classification

Abstract

The non-stationarity of electroencephalogram signals and the variability of different subjects pose major challenges in current Brain-Computer Interfaces research, which requires a time-consuming specific calibration procedure to address. Transfer Learning can make use of data or models from one or more source domains to encourage learning in the target domain, so as to address the challenges of EEG non-stationarity and inter-subject variability. In this paper, a Multi-source domain Transfer Learning Fusion (MTLF) framework is proposed to address the non-stationarity of EEG signals and the complexity of the calibration process. Firstly, the method transforms the source domain data with the resting state segment, so as to decrease the differences between the source domain and the target domain. Then, CSP is used to extract features. After that, Optimal Transport is employed to further refine the source domain data to increase the similarity of the probability distribution between the target and source domains. Finally, an improved transfer learning classifier is employed to classify the target samples. This method does not require the label information of target domain samples and can reduce the calibration workload. The proposed MTLF is assessed on Datasets IIa and IIb of the BCI Competition IV. Compared with seven other algorithms, our method performed relatively the best and achieved mean classification accuracy of 73.69% and 70.83% on Datasets IIa and IIb respectively. Experimental results demonstrate that MTLF can effectively decrease the discrepancy between the source and target domain, and acquire better classification performance on two MI-EEG datasets.