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    Area of Science:

    • Neuroscience
    • Machine Learning
    • Automotive Safety

    Background:

    • Driver reaction time is crucial for road safety.
    • Predicting suboptimal driver performance can enable proactive safety interventions.
    • Electroencephalography (EEG) offers insights into cognitive states influencing reactions.

    Purpose of the Study:

    • To evaluate machine learning strategies for predicting driver reaction times using EEG data.
    • To develop subject-independent models for real-time driver behavior analysis.
    • To predict both specific reaction times and driver response categories (slow/fast).

    Main Methods:

    • Utilized EEG data from 24 drivers in an immersive driving simulator.
    • Extracted spectral features from EEG data preceding road events.
    • Trained and evaluated subject-independent machine learning models.
    • Applied feature engineering strategies to EEG data.

    Main Results:

    • Successfully predicted individual driver reaction times to road events.
    • Achieved accurate classification of drivers as slow or fast responders.
    • Demonstrated the efficacy of using pre-event EEG spectral features.

    Conclusions:

    • EEG-based prediction models can anticipate driver reaction times.
    • This approach supports the development of advanced driver assistance systems.
    • Subject-independent models show promise for real-world safety applications.