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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Библиографические подробности

Главные авторы:	Tianwang Jia, Lubin Meng, Siyang Li, Jiajing Liu, Dongrui Wu
Формат:	Artigo
Язык:	Inglês
Опубликовано:	IEEE 2024-01-01
Серии:	IEEE Transactions on Neural Systems and Rehabilitation Engineering
Предметы:	Brain-computer interface electroencephalogram federated learning motor imagery privacy protection
Online-ссылка:	https://ieeexplore.ieee.org/document/10672548/
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