Human-Machine Interface Evaluation Using EEG in Driving Simulator

TL;DR

This study evaluates different human-machine interface designs in a driving simulator using EEG and subjective measures to assess mental workload, aiming to improve safety and understanding of driver-automation interaction.

Contribution

The paper introduces three novel HMI designs and explores EEG spectral power analysis as a psychophysiological measure of mental workload in driving scenarios.

Findings

Significant differences in mental workload were found among the three HMI designs.

EEG spectral power showed potential but requires further validation as a measure of mental workload.

The developed HMI designs provide a basis for future psychophysiological research in driving safety.

Abstract

Automated vehicles are pictured as the future of transportation, and facilitating safer driving is only one of the many benefits. However, due to the constantly changing role of the human driver, users are easily confused and have little knowledge about their responsibilities. Being the bridge between automation and human, the human-machine interface (HMI) is of great importance to driving safety. This study was conducted in a static driving simulator. Three HMI designs were developed, among which significant differences in mental workload using NASA-TLX and the subjective transparency test were found. An electroencephalogram was applied throughout the study to determine if differences in the mental workload could also be found using EEG's spectral power analysis. Results suggested that more studies are required to determine the effectiveness of the spectral power of EEG on mental workload, but the three interface designs developed in this study could serve as a solid basis for future research to evaluate the effectiveness of psychophysiological measures. Marie Sklodowska-Curie Actions; Innovative Training Network (ITN); SHAPE-IT; Grant number 860410; Publication date: [27 July 2023]; DOI: [10.1109/IV55152.2023.10186567]