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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| Main Authors: | , , , , |
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| Format: | Book |
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IEEE,
2024-01-01T00:00:00Z.
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A1234.567 |
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