Sub-band target alignment common spatial pattern in brain-computer interface. (August 2021)

• Record Type: Journal Article
• Authors: Zhang, Xianxiong; She, Qingshan; Chen, Yun; Kong, Wanzeng; Mei, Congli
• Is Part Of: Computer methods and programs in biomedicine. Volume 207(2021)
• Publication Date: 2021-08
• Subjects: Brain-computer interface -- Transfer learning -- Sub-band filtering -- Target alignment -- Cross-subject classification; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106150
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Transfer learning can reduce the sample calibration time. Target alignment make the distribution of samples in source and target domains more similar. Combining Sub-band filtering and target alignment obtain richer frequency information. The classification performance of this method is optimal. Abstract: Background and objective: In the brain computer interface (BCI) field, using sub-band common spatial pattern (SBCSP) and filter bank common spatial pattern (FBCSP) can improve the accuracy of classification by selection a specific frequency band. However, in the cross-subject classification, due to the individual differences between different subjects, the performance is limited. Methods: This paper introduces the idea of transfer learning and presents the sub-band target alignment common spatial pattern (SBTACSP) method and applies it to the cross-subject classification of motor imagery (MI) EEG signals. First, the EEG signals are bandpass-filtered into multiple frequency bands (sub-band filtering). Subsequently, the source domain trails are aligned into the target domain space in each frequency band. The CSP algorithm is then employed to extract features among which more representative features are selected by the minimum redundancy maximum relevance (mRMR) approach from each sub-band. Then the features of all sub-bands are fused. Finally, conventional linear discriminant analysis (LDA) algorithm is used for MI classification. Results: Our method is evaluated on Datasets Ⅱa and Ⅱb of the BCI Competition Ⅳ. Compared with six state-of-the-art algorithms, the proposed SBTACSP method performed relatively the best and achieved a mean classification accuracy of 75.15% and 66.85% in cross-subject classification of Datasets Ⅱa and Ⅱb respectively. Conclusion: Therefore, the combination of sub-band filtering and transfer learning achieves superior classification performance compared to either one. The proposed algorithms will greatly promote the practical application of MI based BCIs.