Juggler’s ASR: Unpacking the principles of artifact subspace reconstruction for revision toward extreme MoBI
Bibliographic record
Abstract
To improve the Artifact Subspace Reconstruction (ASR) algorithm's performance for real-world EEG data by addressing the problem of low-quality or no calibration data identification in the original ASR (ASR original ) algorithm. We proposed a new method for defining high-quality calibration data using point-by-point amplitude evaluation to eliminate collateral rejection of clean data, which is identified as the major cause of the problem with ASR original . We compared non-parametric and parametric approaches, namely Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the Generalized Extreme Value (GEV) distribution (ASR DBSCAN and ASR GEV , respectively). We demonstrated the effectiveness of these approaches on simulated and real EEG data. Simulation results showed that ASR DBSCAN and ASR GEV removed simulated artifacts completely where ASR original failed, both in time- and frequency-domain evaluations. In empirical data from 205-channel EEG recordings during a three-ball juggling task (n=13), ASR DBSCAN found 42% and ASR GEV found 24% of data usable for calibration on average, compared to only 9% by ASR original . Subsequent Independent Component Analysis (ICA) showed that data preprocessed with ASR DBSCAN and ASR GEV produced brain ICs that accounted for more variance of the original data (30% and 29%) compared to ASR original (26%). The proposed ASR DBSCAN and ASR GEV methods handle motion-related artifacts better than the original ASR algorithm, enabling researchers to better extract brain activity during real-world motor tasks. These methods provide a practical advantage in processing EEG data from experiments involving high-intensity motor activities, advancing biomedical research capabilities. • We developed new algorithms that can handle high-frequency motion artifacts when using artifact subspace reconstruction (ASR). • The proposed methods better handle EEG data with highly non-stationary noise typically due to high-intensity motor execution under the real-world conditions. • We clarified the reason, at least in part, why a counterintuitively large number of standard deviations has been recommended as a cutoff threshold in the literature on ASR applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".