The Moving Wave: Applications of the Mobile EEG Approach to Study Human Attention.
Bibliographic record
Abstract
While historically confined to isolated research laboratories, electroencephalography (EEG) paradigms can finally be used in studies involving walking and other complex behaviors. This transition from isolated/immobile to unstructured mobile research can open new doors to understanding attentional processes as they occur naturally. However, there are current limitations in mobile EEG that must be overcome to achieve great quality signals. We examine the feasibility of mobile paradigms, including ecological validity, artifact correction techniques, and methodological considerations. We review several mobile studies related to attentional demands. This includes the replication of robust effects like the P3 in mobile paradigms in our lab, studies using walking, cycling, and dual tasking to study attention. We discuss how the mobile approach compliments traditional laboratory paradigms while it can add new dimensions to cognitive and attentional research. We discuss promising applications of portable EEG in workplace safety and other areas including road safety, rehabilitation medicine, and brain-computer interfaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".