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.
An improved feature extraction algorithms of EEG signals based on motor imagery brain-computer interface
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
eess.SP 1years
2026 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
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.