EEG assessment of driving cognitive distraction caused by central control information

Zhao, Yan; Yang, Xiaonan; Wang, Yahui; Wei, Wanni; Zuo, Heng; Yu, Yuan; Zhao, Yan · 2023 · Crossref

DOI: 10.54941/ahfe1003011

archive: archived pipeline: cataloged verified

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Summary

This study addresses the challenge of assessing cognitive distraction in drivers caused by in-vehicle central control systems. As automotive technology advances, central control interfaces provide essential functions but also introduce complex information that can distract drivers, increasing accident risk. While cognitive distraction is a known safety hazard, existing assessment methods often lack objective physiological metrics. The authors propose using electroencephalogram (EEG) data to provide a more accurate and scientific evaluation of driver cognitive load, aiming to inform the design of safer in-vehicle infotainment systems. The experimental design involved 30 participants with normal or corrected vision, divided into two groups: one for establishing evaluation thresholds and another for verifying the model’s usability with touch-based interfaces. Participants engaged in a driving simulation using a SCANeR studio environment and a Huawei tablet mounted as a central control screen. The cognitive distraction task required drivers to identify and report the frequency of specific target sounds (A, A/B, or A/B/C) while driving, creating three levels of increasing difficulty. EEG signals were recorded from 18 electrodes, preprocessed to remove noise, and analyzed for power spectral density across six frequency bands (Delta, Theta, Alpha, Beta1, Beta2, Gamma) in six brain regions. Repeated measures ANOVA and post-hoc LSD tests were used to analyze the impact of task difficulty on brain wave activity and driving performance metrics, such as lane lateral excursion. The results demonstrated that Theta, Beta1, and Beta2 brain waves in the frontal pole and central regions significantly reflected changes in cognitive load. As task difficulty increased from level 1 to 2 and 3, Theta and Beta2 waves in these regions gradually decreased in activity, while Beta1 waves became more active. Statistical analysis confirmed significant differences in the power spectral density of these waves between difficulty levels 1 and 2, and 1 and 3, though no significant difference was found between levels 2 and 3. Behavioral data showed a similar pattern, with significant differences in lane lateral excursion between lower and higher difficulty levels. The authors attribute the lack of difference between levels 2 and 3 to participants actively abandoning the cognitive subtask at the highest difficulty to maintain driving safety, thereby stabilizing their cognitive load. The study concludes that EEG-based assessment, specifically monitoring Theta, Beta1, and Beta2 waves in frontal and central regions, offers an objective method for evaluating cognitive distraction caused by central control information. This approach provides a scientific basis for optimizing in-vehicle interface designs to minimize driver distraction. By combining EEG data with behavioral metrics and driver feedback, manufacturers can better understand the neural mechanisms of cognitive processes during driving, leading to safer human-machine interaction designs in intelligent transportation systems.

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Topics

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• neuro workload indices
• cognitive
• cognitive capacity variation
• mental demand
• mind wandering
• distraction detection algorithms

Information type

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• Empirical Findings: physiological data
• Theoretical Contribution: conceptual framework, theory or model