Cross-Subject Brain–Computer Interfaces with Joint Distribution Alignment

• Published in: Journal of circuits, systems, and computers Vol. 33; no. 12
• Main Authors: Zhao, Xianghong, Ma, Longhua, Cai, Weiming, Lian, Bin, Cui, Jialin, Ye, Lingjian
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 01.08.2024; World Scientific Publishing Co. Pte., Ltd
• Subjects: Algorithms; Human-computer interface; Support vector machines; brain–computer interface; joint distribution discrepancy; domain adaptation; Cross-subject; joint distribution alignment; motor-imagery
• ISSN: 0218-1266; 1793-6454
• DOI: 10.1142/S0218126624502050

Distributions of electroencephalogram (EEG) data vary greatly across different subjects. It is a very important issue how to generalize models across subjects. In this paper, an algorithm is proposed to build high-performance cross-subject motor-imagery brain–computer interfaces (BCIs) for a new subject. First, a novel distance metric is proposed to quantify the joint distribution discrepancy (JDD) between data from different subjects. It gives better evaluations for discrepancies between different distributions than conventional probabilistic metrics. Moreover, it can be extended to design many novel algorithms. Second, a support vector machine combined with JDD (JDMSVM) is proposed for cross-subject classification. For dataset dataIVa, the JDMSVM runs best under 9 out of 15 situations and averagely outperforms counterparts by 10.1%, 9.5%, 3.2% and 1.7%, respectively. For GigaDataset, JDMSVM runs best under 8 of 12 conditions. It averagely outperforms its counterparts by 10.4%, 5.3%, 2.7% and 2.4%, respectively. The experiments demonstrate that the proposed algorithm is effective and competitive for cross-subject BCI.