计算机科学
考试(生物学)
人工智能
最大化
语音识别
数学
数学优化
生物
古生物学
作者
Siyang Li,Ziwei Wang,Hanbin Luo,Lieyun Ding,Dongrui Wu
出处
期刊:IEEE Transactions on Biomedical Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-08-08
卷期号:71 (2): 423-432
被引量:7
标识
DOI:10.1109/tbme.2023.3303289
摘要
An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available.
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