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Multi-Query Cross-Modal Attention Fusion for Cognitive Impairment Recognition.

Minghui Zhao, Hongxiang Gao, Xinru Qi

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |June 25, 2025
    PubMed
    Summary

    Early recognition of cognitive impairment in the elderly is crucial. A new multimodal fusion model using synchronized EEG, ECG, and video signals shows promise for accurate and rapid assessment in cognitive rehabilitation.

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    Area of Science:

    • Gerontology
    • Neuroscience
    • Biomedical Engineering

    Background:

    • Cognitive impairment affects the elderly, presenting societal and healthcare challenges.
    • Early detection is vital for effective cognitive rehabilitation and management.
    • Current assessment methods lack convenience and speed.

    Purpose of the Study:

    • To develop a novel multimodal fusion model for accurate and rapid cognitive impairment recognition.
    • To leverage synchronized electroencephalography (EEG), electrocardiography (ECG), and video signals.
    • To enhance early detection for improved cognitive rehabilitation strategies.

    Main Methods:

    • Constructed the EEV-CI dataset with synchronized EEG, ECG, and video data.
    • Developed a frequency-band adaptive encoder for physiological signals.
    • Utilized a multi-query cross-modal attention mechanism for signal fusion.
    • Extracted facial action units and emotional states for expression analysis.

    Main Results:

    • The proposed model achieved 87.01% accuracy, 78.17% F1-macro, 86.88% AUC, and 57.42% MCC.
    • Modality-specific experiments confirmed the contribution of each signal type.
    • Demonstrated superior performance in cognitive impairment recognition.

    Conclusions:

    • Multimodal fusion is effective for cognitive impairment assessment.
    • The developed model offers valuable clinical support for rehabilitation.
    • Highlights the potential for rapid and convenient cognitive impairment detection.