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Related Experiment Videos

Mamba-Based Enhanced Multimodal Emotion Recognition with EEG Guidance.

Xiangle Ping, Wenhui Huang, Yuanjie Zheng

    IEEE Journal of Biomedical and Health Informatics
    |June 12, 2026
    PubMed
    Summary
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    This study introduces MambaMER, a novel approach for multimodal emotion recognition using electroencephalography (EEG) and eye movements. MambaMER effectively suppresses conflicting data, improving emotion recognition accuracy by leveraging Mamba

    Area of Science:

    • Neuroscience
    • Computer Science
    • Artificial Intelligence

    Background:

    • Multimodal emotion recognition combines data from various sources for better accuracy.
    • Existing methods struggle with irrelevant or conflicting information across modalities.
    • Mamba's efficiency in filtering data and modeling long-range dependencies inspires new approaches.

    Purpose of the Study:

    • To propose MambaMER, a Mamba-based paradigm for enhanced EEG-guided multimodal emotion recognition.
    • To address performance limitations caused by cross-modal information conflicts.
    • To improve the accuracy and efficiency of emotion recognition systems.

    Main Methods:

    • Developed a multi-scale EEG-guided conflict suppression module using a selective cross state space model.

    Related Experiment Videos

  • Engineered a novel cross-modal fusion mechanism (Mutual-Cross-Mamba and Merge-Mamba).
  • Utilized Mamba's selective mechanism for dynamic parameter adjustment to filter irrelevant data.

    • Main Results:

    • The proposed method significantly suppresses irrelevant or conflicting information.
    • Achieved enhanced integration of complementary features between EEG and eye movement modalities.
    • Demonstrated superior performance over state-of-the-art methods on SEED, SEED-IV, and SEED-V datasets.

    Conclusions:

    • MambaMER effectively overcomes cross-modal conflicts in multimodal emotion recognition.
    • The approach enhances recognition accuracy and maintains linear computational complexity.
    • This paradigm offers a promising direction for advanced emotion recognition research.