EEG-fNIRS Fusion Approach for the Measurement of Workplace Stress Related to Design and Noise
Bibliographic record
Abstract
This study aims to evaluate the effect of workstation types, such as ergonomic versus non-ergonomic and quiet versus noisy, on neural-vascular functions and networks of the prefrontal cortex (PFC) underlying the cognitive activity involved during mental stress. Workstation design and noise have been reported to affect the physical and mental health of employees. However, while the functional effects of workstation types have been documented, there is little research on their influence on brain executive functions. The electroencephalography (EEG) and functional near-infrared \nspectroscopy (fNIRS) were used to simultaneously measure electrical activity and hemoglobin concentration changes in the PFC. The multimodal signals were collected from 23 healthy adults who completed the Montreal imaging stress task in ergonomic and non-ergonomic workstations; and 25 adults in quiet and noisy workplaces. A supervised machine learning method with multimodal coupling based on temporally embedded canonical correlation analysis (mCtCCA) was utilized to obtain the association between neural activity and local changes in hemoglobin concentrations to enhance \nlocalization and accuracy and to generate neurovascular coupling networks.
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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 | 0.001 | 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 | 0.000 |
| 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".