EEG Features to Quantify the NASA-TLX Factors of Cognitive Workload
Bibliographic record
Abstract
Measuring cognitive workload (CWL) is crucial for dynamic task reallocation (i.e., adaptation) between a human and a machine in a human-machine system (HMS). A conventional measurement of the CWL is based on subjectively reported scores about the six factors of the NASA Task Load Index (NASA-TLX) questionnaire. The questionnaire cannot however capture real-time fluctuations of the factors for an objective quantification. Additionally, each of the factors is associated with distinct activities and can be influenced by individual characteristics and/or task contexts. Such HMS adaptation should thus consider the objective quantification of each factor. So far, the quantification remains largely unexplored, while existing studies reveal a potential use of an electroencephalography (EEG) in measuring the CWL levels (e.g., high, medium, and low). Herein, we presented a pioneering study to propose EEG features for quantifying the factors. The pertinence of the features was demonstrated by their strong correlations with the scores of the factors across three distinct cases of visuomotor tasks. The pertinence is the stepping stone toward factor-based interventions in enabling HMS adaptation.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".