Understanding Mental States in Active and Autonomous Driving with EEG

• URL: http://arxiv.org/abs/2512.09190v1
• Date: Tue, 09 Dec 2025 23:30:52 GMT
• Title: Understanding Mental States in Active and Autonomous Driving with EEG
• Authors: Prithila Angkan, Paul Hungler, Ali Etemad,
• Abstract summary: This paper presents the first EEG-based comparison of cognitive load, fatigue, and arousal across the two driving modes.<n>Although both modes evoke similar trends across complexity levels, the intensity of mental states and the underlying neural activation differ substantially.
• Score: 33.344042380346245
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Understanding how driver mental states differ between active and autonomous driving is critical for designing safe human-vehicle interfaces. This paper presents the first EEG-based comparison of cognitive load, fatigue, valence, and arousal across the two driving modes. Using data from 31 participants performing identical tasks in both scenarios of three different complexity levels, we analyze temporal patterns, task-complexity effects, and channel-wise activation differences. Our findings show that although both modes evoke similar trends across complexity levels, the intensity of mental states and the underlying neural activation differ substantially, indicating a clear distribution shift between active and autonomous driving. Transfer-learning experiments confirm that models trained on active driving data generalize poorly to autonomous driving and vice versa. We attribute this distribution shift primarily to differences in motor engagement and attentional demands between the two driving modes, which lead to distinct spatial and temporal EEG activation patterns. Although autonomous driving results in lower overall cortical activation, participants continue to exhibit measurable fluctuations in cognitive load, fatigue, valence, and arousal associated with readiness to intervene, task-evoked emotional responses, and monotony-related passive fatigue. These results emphasize the need for scenario-specific data and models when developing next-generation driver monitoring systems for autonomous vehicles.

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