Correlating Motion Artifacts in Wet and Dry Electrodes With Head Kinematics During Physical Activities in Ambulatory EEG Monitoring
Bibliographic record
Abstract
Electroencephalography (EEG) is a common neuroimaging technique used in clinical, research, and consumer technology given its non-invasiveness, high temporal resolution, and sensitivity. The main challenge in ambulatory EEG monitoring is the presence of substantial motion artifacts, especially for high intensity movements such as running. This study compared motion artifacts in gold cup, gel, and dry electrodes during different levels of physical activity. Eight healthy participants were instrumented with a wearable EEG cap and an instrumented mouthguard to measure head kinematics. While all electrode types performed similarly in resting state, dry electrodes showed high-amplitude motion artifacts and worse performance during walking, running, and jumping. EEG-motion coherence was found to be higher for dry electrodes in low-intensity movements (e.g., walking with 0.1-0.5 g head acceleration, where 1g = 9.81 m/s2), but similar across the 3 electrode types for medium to high intensity movements (e.g., running and jumping with 0.5-3.5 g head acceleration), with peak coherences at up to 20 Hz. Fast walk trials showed the highest linear correlation between EEG signal and head acceleration, indicating potentially better coupling between the head and the electrode at this movement intensity. Our results imply that dry electrodes require further hardware development to mitigate motion artifacts before reliable application in ambulatory monitoring, and wet electrodes also exhibit clear movement-related artifacts in higher intensity movements. Uniquely, we identified EEG-motion coupling as a contributing factor to motion artifact characteristics, and coherence analysis may be a promising approach for motion artifact identification and quantification. Further characterization, quantification, and mitigation of motion artifacts will aid in higher quality measurement and meaningful interpretation of ambulatory EEG signals for clinical and research 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".