Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces

Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classificat...

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發表在:	IEEE Transactions on Neural Systems and Rehabilitation Engineering
Main Authors:	Tianwang Jia, Lubin Meng, Siyang Li, Jiajing Liu, Dongrui Wu
格式:	Article
語言:	英语
出版:	IEEE 2024-01-01
主題:	Brain-computer interface electroencephalogram federated learning motor imagery privacy protection
在線閱讀:	https://ieeexplore.ieee.org/document/10672548/