pith:V2K2SURN
From EEG Cleaning to Decoding: The Role of Artifact Rejection in MI-based BCIs
Automated EEG artifact rejection improves motor imagery BCI decoding most for low-SNR subjects and reduces performance gaps across users.
arxiv:2605.12408 v2 · 2026-05-12 · eess.SP
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Claims
We show rejection effects are strongly subject- and regime-dependent, with the largest gains in low-baseline/low-SNR conditions, so it should be used adaptively. FAAR reduces inter-subject performance variability, an important property for MI-BCI reliability and BCI-illiteracy, without aggressive data removal.
That the compact artifact-sensitive features and derived Signal Quality Index reliably identify contaminated epochs across diverse MI datasets and artifact types without any prior knowledge or manual tuning, and that observed performance gains are attributable to the rejection policy rather than dataset-specific factors.
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.
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Receipt and verification
| First computed | 2026-05-25T02:01:23.680680Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
ae95a9522d9668b376a879ce3ab03abe27085f86ffeeb9211b662e6379a10fcf
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/V2K2SURNSZULG5VIPHHDVMB2XY \
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Canonical record JSON
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