On the Analysis of EEG Features for Mental Workload Assessment During Physical Activity
Bibliographic record
Abstract
Assessment of mental workload is crucial for applications which require constant attention and where conditions such as mental fatigue and drowsiness must be avoided. As such, electroencephalography (EEG) based mental workload models have been developed in the past. The majority of these models, however, have assumed individuals are not ambulant, thus bypassing the issue of movement-related EEG artefacts. While such models may be useful for a number of applications (e.g., operators are sitting), they may not apply in situations in which operators are performing their task under different physical activity levels. Representative examples can include first responders, such as paramedics, firefighters, or police officers. In this work, we take the first steps towards overcoming this limitation and present results of an experiment simultaneously eliciting increasing mental workload states at varying physical activity levels. EEG data from forty-seven participants was collected while they performed the NASA Revised Multi-Attribute Task Battery II (MATB-II) under three different activity level conditions (no, medium, high). In this study, we report the effects of activity on the noise-robustness and distribution of several spectral, amplitude/phase coherence, and amplitude modulation features, with the ultimate goal of deriving a feature set tailored towards automated workload assessment during physical activity. Preliminary results show spectral features acquired from the frontal area of the cortex as the most promising and that activity aware mental workload models should be developed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.009 | 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 teacher head, 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".