FAAR is a new automated artifact rejection method using compact features and adaptive Signal Quality Index thresholds that improves MI-BCI performance most in low-baseline conditions and reduces inter-subject variability across 13 public datasets.
Reliable and fast automatic artifact rejection of long-term EEG recordings based on Isolation Forest
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From EEG Cleaning to Decoding: The Role of Artifact Rejection in MI-based BCIs
FAAR is a new automated artifact rejection method using compact features and adaptive Signal Quality Index thresholds that improves MI-BCI performance most in low-baseline conditions and reduces inter-subject variability across 13 public datasets.