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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| Hauptverfasser: | , , , , |
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| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
IEEE
2024-01-01
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| Schriftenreihe: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Schlagworte: | |
| Online-Zugang: | https://ieeexplore.ieee.org/document/10672548/ |
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