Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 21; p. 6321
• Main Authors: Zhang, Kai, Xu, Guanghua, Zheng, Xiaowei, Li, Huanzhong, Zhang, Sicong, Yu, Yunhui, Liang, Renghao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.11.2020; MDPI
• Subjects: Accuracy; Algorithms; Brain; Brain-Computer Interfaces; Calibration; Classification; Datasets; decoding; EEG; Electroencephalography; Humans; Knowledge; Machine Learning; Physiology; Probability distribution; Review; Signal processing; transfer learning; transfer learning; classification; EEG; review; decoding

The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the same probability distribution. However, this may be...