High Cognitive Load Assessment in Drivers Through Wireless Electroencephalography and the Validation of a Modified <i>N</i>-Back Task
Bibliographic record
Abstract
This paper explores the influence of high cognitive load on vehicle driver's electroencephalography (EEG) signals collected from two channels (Fp1, Fp2) using a wireless consumer-grade system. Although EEG has been used in driving-related research to assess cognitive load, only a few studies focused on high load, and they used research-grade systems. Recent advancements allow for less intrusive and more affordable systems. As an exploration, we tested the feasibility of one such system to differentiate among three levels of cognitive taskload in a simulator study. Thirty-seven participants completed a baseline drive with no secondary task and two drives with a modified version of the n-back task (1-back and 2-back). The modification removed the verbal response required during task presentation to prevent EEG-signal degradation, with the 2-back task expected to impose higher load than that by the 1-back task. Another objective of this study is to validate that this modified task increased the cognitive load in the expected manner. The modified task led to significant trends from baseline to 1-back, and from 1-back to 2-back in participants' heart rate, galvanic skin response, respiration, horizontal gaze position variability, and pupil diameter, all in line with the previous driving-related studies on cognitive load. Furthermore, the EEG system was observed to be sensitive to the modified task, with the power of alpha band decreasing significantly with increasing n-back levels (baseline versus 1-back: 0.092 Bels on Fp1, 0.179 on Fp2; 1-back versus 2-back: 0.209 on Fp1, 0.147 on Fp2). Thus, a consumer-grade EEG system has the potential to capture high levels of cognitive load experienced by drivers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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".