Adaptive deep feature representation learning for cross-subject EEG decoding

• Published in: BMC bioinformatics Vol. 25; no. 1; pp. 393 - 19
• Main Authors: Liang, Shuang, Li, Linzhe, Zu, Wei, Feng, Wei, Hang, Wenlong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 31.12.2024; BioMed Central; BMC
• Subjects: Adaptation; Adaptive sampling; Algorithms; Brain-Computer Interfaces; Calibration; Classification; Clusters; Datasets; Decoders; Decoding; Deep Learning; Discriminative feature learning; Domain adaptation; EEG; Effectiveness; Electroencephalogram; Electroencephalography; Electroencephalography - methods; Electronic data processing; Entropy minimization; Euclidean space; Humans; Machine learning; Mental task performance; Methods; Motor imagery; Motor skill learning; Motor task performance; Performance evaluation; Regularization; Reinforcement learning (Machine learning); Representations; Signal Processing, Computer-Assisted; China; Domain adaptation; Motor imagery; Electroencephalogram; Discriminative feature learning; Entropy minimization

The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other...