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    Area of Science:

    • Neuroscience
    • Cognitive Science
    • Biomedical Engineering

    Background:

    • Real-time monitoring of sustained attention is crucial for task performance and safety.
    • EEG-based brain-computer interfaces (BCIs) are effective for monitoring human states.
    • Existing unimodal EEG features have limitations in signal quality and interference susceptibility.

    Purpose of the Study:

    • To develop and validate a multimodal BCI approach for real-time sustained attention monitoring.
    • To investigate the fusion of event-related potentials (ERPs), steady-state visual evoked potentials (SSVEPs), and spontaneous oscillations.
    • To optimize visual stimulation parameters for maximal attentional modulation.

    Main Methods:

    • Developed a continuous go/no-go task to elicit ERPs, SSVEPs, and spontaneous oscillations.
    • Integrated contrast-response functions for SSVEP attention modulation across 12 stimulus contrast levels.
    • Applied multimodal feature fusion combining spontaneous beta-band oscillations and SSVEP responses.

    Main Results:

    • Sustained attention decline was predictable before behavioral errors in a continuous task.
    • Optimal classification performance (90.83% best, 74.48% average) was achieved at 31.60% stimulus contrast.
    • Fused features significantly improved attention state monitoring accuracy compared to unimodal approaches.

    Conclusions:

    • Multimodal feature fusion in EEG-based BCIs offers a robust method for real-time attention monitoring.
    • This approach can predict attention lapses, enhancing safety and performance in complex tasks.
    • Findings pave the way for advanced, reliable BCI systems for cognitive state assessment.