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HATNet: EEG-Based Hybrid Attention Transfer Learning Network for Train Driver State Detection.

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    This study introduces HATNet, a novel transfer learning model for train driver state detection using electroencephalography (EEG). HATNet significantly improves accuracy by leveraging hybrid attention mechanisms and a unique transfer learning strategy.

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    Area of Science:

    • Neuroscience
    • Machine Learning
    • Transportation Safety

    Background:

    • Electroencephalography (EEG) is crucial for train driver state detection, offering high accuracy and low latency.
    • Current EEG methods for driver status detection underutilize rich physiological data, and a lack of datasets for abnormal driver states exists.
    • Addressing these limitations is vital for enhancing railway safety and operational efficiency.

    Purpose of the Study:

    • To propose a novel transfer learning model, HATNet, integrating a hybrid attention mechanism for improved EEG-based train driver state detection.
    • To develop a new EEG dataset specifically for abnormal states of train drivers.
    • To evaluate HATNet's performance against state-of-the-art models in both subject-dependent and subject-independent scenarios.

    Main Methods:

    • EEG signals were segmented into patches, and a hybrid attention module was employed to capture local and global temporal patterns.
    • A channel-wise attention module was introduced to establish spatial representations among EEG channels.
    • A calibration-based transfer learning strategy was utilized for efficient adaptation to new subject data.

    Main Results:

    • HATNet achieved superior classification accuracy (94.26% subject-dependent, 87.03% subject-independent) compared to state-of-the-art end-to-end models.
    • The hybrid attention module demonstrated effectiveness in capturing temporal semantic information from EEG data.
    • A novel EEG dataset for abnormal train driver states was successfully established via a multistimulus oddball experiment.

    Conclusions:

    • HATNet represents a significant advancement in EEG-based train driver state detection, outperforming existing methods.
    • The hybrid attention mechanism and transfer learning strategy are key to HATNet's enhanced performance.
    • The developed dataset and HATNet model contribute to improved railway safety and driver monitoring technologies.