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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Hybrid Brain-computer interfaces (BCI) integrate electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) for comprehensive brain activity detection, leveraging EEG's temporal and fNIRS's spatial resolution.
    • Current integration methods struggle to capture spatiotemporal coupling features and inter-modality correlations, often resulting in unrefined multimodal representations.
    • Holistic learning paradigms in existing methods lead to redundant feature extraction, limiting the discriminative power of hybrid BCI systems.

    Purpose of the Study:

    • To propose a novel Disentangled Multimodal Spatiotemporal Learning (DMSL) method for hybrid EEG-fNIRS BCI systems.
    • To enhance the extraction of spatiotemporal coupling features and inter-modality correlations between EEG and fNIRS signals.
    • To develop a unified framework for disentangled representation learning and multimodal spatiotemporal coupling.

    Main Methods:

    • DMSL employs a compact convolutional module with 1D temporal and spatial convolutions to extract spatiotemporal patterns from individual modalities.
    • A multimodal attention interaction module captures inter-modality correlations, refining modality-specific representations.
    • An adaptive multi-branch graph convolutional module, utilizing reconstructed channels and modality constraints, disentangles common and specific representations for effective fusion and task prediction.

    Main Results:

    • The proposed DMSL method achieved state-of-the-art performance on mental arithmetic, motor imagery, and emotion recognition tasks.
    • DMSL outperformed existing methods by 2.34% in mental arithmetic, 0.59% in motor imagery, and 1.47% in emotion recognition.
    • The results validate the effectiveness of DMSL in improving EEG-fNIRS decoding accuracy and demonstrating strong generalization capabilities.

    Conclusions:

    • The DMSL method significantly enhances the decoding performance of hybrid EEG-fNIRS BCI systems.
    • The disentangled representation learning approach effectively captures crucial spatiotemporal coupling features and inter-modality correlations.
    • DMSL shows promising potential for advancing BCI applications requiring robust and accurate brain activity interpretation.