FAAR is a new lightweight automated artifact rejection method for EEG that improves motor imagery BCI decoding in low-SNR conditions, reduces inter-subject variability across 13 public datasets, and supports real-time use without manual tuning.
Multiclass brain- computer interface classification by Riemannian geometry
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From EEG Cleaning to Decoding: The Role of Artifact Rejection in MI-based BCIs
FAAR is a new lightweight automated artifact rejection method for EEG that improves motor imagery BCI decoding in low-SNR conditions, reduces inter-subject variability across 13 public datasets, and supports real-time use without manual tuning.