Semi-supervised multi-source transfer learning for cross-subject EEG motor imagery classification

• Published in: Medical & biological engineering & computing Vol. 62; no. 6; pp. 1655 - 1672
• Main Authors: Zhang, Fan, Wu, Hanliang, Guo, Yuxin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2024; Springer Nature B.V
• Subjects: Attention; Attention task; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Classification; Computer Applications; data collection; EEG; Electroencephalography; Human Physiology; Human-computer interface; Image classification; Imaging; Invariants; Mental task performance; Motor skill learning; Original Article; Radiology; Representations; Semi-supervised learning; Signal classification; Transfer learning; Multi-source transfer learning; Brain-computer interfaces; Motor imagery; Electroencephalogram; Semi-supervised learning; Dynamic weighting

Electroencephalogram (EEG) motor imagery (MI) classification refers to the use of EEG signals to identify and classify subjects’ motor imagery activities; this task has received increasing attention with the development of brain-computer interfaces (BCIs). However, the collection of EEG data is usua...