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.
Enhanced detection of arti- facts in EEG data using higher-order statistics and independent component analysis
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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.