MDTL: A Novel and Model-Agnostic Transfer Learning Strategy for Cross-Subject Motor Imagery BCI

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 31; p. 1
• Main Authors: Li, Ang, Wang, Zhenyu, Zhao, Xi, Xu, Tianheng, Zhou, Ting, Hu, Honglin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Brain; Brain modeling; Brain-computer interface; Computational modeling; Computer applications; Datasets; Deep learning; Domains; EEG; Electroencephalography; Feature extraction; Human-computer interface; Imagery; Implants; Machine learning; Mental task performance; Model accuracy; Motor imagery; Motor skill learning; Motor task performance; Neural networks; Solid modeling; Transfer learning

In recent years, deep neural network-based transfer learning (TL) has shown outstanding performance in EEG-based motor imagery (MI) brain-computer interface (BCI). However, due to the long preparation for pre-trained models and the arbitrariness of source domain selection, using deep transfer learni...