Human-Machine Interface Evaluation Using EEG in Driving Simulator
DOI: 10.1109/iv55152.2023.10186567
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This study addresses the critical need for objective evaluation methods for Human-Machine Interfaces (HMIs) in automated vehicles (AVs). While AVs promise improved safety, current HMI designs often confuse users regarding their responsibilities during automation transitions. Existing evaluation methods rely heavily on subjective heuristics, lacking standardized, objective metrics suitable for real-time assessment. The research investigates whether electroencephalogram (EEG) spectral power analysis can detect differences in mental workload across distinct HMI designs, aiming to validate EEG as a psychophysiological measure for HMI transparency assessment. The researchers developed three SAE Level 2 HMI designs varying in transparency: "Fog" (low clarity, poor system feedback), "Trans" (high clarity, clear system feedback), and "Trans-fog" (high interface clarity, poor system feedback). Twelve participants with valid driving licenses underwent trials in a static driving simulator. Each participant experienced all three designs in a counterbalanced order across four traffic layouts, totaling 12 trials. Mental workload was measured subjectively using the NASA-Task Load Index (NASA-TLX) and a subjective transparency questionnaire. Objectively, 32-channel EEG data were recorded and preprocessed using adaptive mixture independent component analysis. The study analyzed relative mean spectral power in the alpha (8–12 Hz) and theta (4–7 Hz) bands during L2 automation activation events. Subjective evaluations confirmed significant differences in mental workload and transparency among the designs. The "Trans" HMI yielded the lowest NASA-TLX scores and highest transparency ratings, indicating it was the easiest to understand. The "Fog" HMI resulted in significantly higher workload and lower transparency compared to "Trans." Notably, while "Trans" and "Trans-fog" showed no difference in subjective transparency scores, "Trans" had significantly lower mental workload, highlighting the importance of system transparency (feedback logic) alongside interface design. However, the EEG analysis failed to detect significant differences in relative alpha or theta power across the three HMI designs. Although the direction of the means aligned with literature expectations (higher alpha for lower workload), the statistical tests showed no significance. The study concludes that while the three HMI designs successfully elicited distinct subjective workloads, EEG spectral power analysis was not effective in distinguishing these differences in this experimental setup. The authors attribute this null result to low statistical power caused by a small sample size and the aggregation of EEG epochs, which may have averaged out subtle neural signals. The findings underscore that both interface and system transparency are crucial for reducing driver workload. The validated HMI designs serve as a basis for future research to refine psychophysiological measurement techniques, potentially by analyzing separated epochs or recruiting larger samples, to enable efficient, objective HMI evaluation in real driving scenarios.
Key finding
Subjective evaluations detected significant differences in mental workload among the HMI designs, but EEG spectral power analysis did not yield statistically significant results.
Methodology
simulator
Sample size: 10
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-05 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-06-06 |
| promote | success | — | — | — | 1 | 2026-06-05 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Applied Guidance: design guidelines
- Empirical Findings: physiological data
- Methodological Resource: tool software