Contrast-based artifact removal enables microstate analysis in ambulatory EEG
Bibliographic record
Abstract
OBJECTIVE: Recent advances in electroencephalography (EEG) technology present new opportunities for mobile neuroimaging and real-world human neuroscience studies. However, EEG is sensitive to many sources of artifacts, and it can be especially difficult to remove high amplitude motion artifacts. METHODS: We demonstrate a novel method using generalized eigen-decomposition (GED) for artifact removal, validated this approach using semi-simulated and real artifactual EEG collected during walking and jogging, and showed the feasibility of using cleaned ambulatory EEG data for brain microstate analysis. RESULTS: We found that GED is effective even in ultra-low SNR (0.1 - 5) conditions, achieving a correlation of 0.93 and RMSE of 1.43 ${\bm{\mu V}}$ in recovering ground truth activity using semi-simulated data, and increased the number of brain components by 10.9 and 11.8 for real data. GED showed superior performance compared with artifact subspace reconstruction (ASR) and independent component analysis (ICA) methods on semi-simulated data in very low SNR regimes. Using cleaned data, we were able to extract canonical EEG microstates across all tasks and sessions for examining task-related modulation in microstate duration, occurrence, and time coverage. We observed increased duration, occurrence and time coverage of microstates A and duration of microstate B, and decreased occurrence and time coverage of microstate D during motion compared with rest, corresponding to heightened alertness and increased visual processing. CONCLUSION: These findings not only validate our artifact removal approach but also open new avenues for investigating neural dynamics during naturalistic human behavior.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".