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Related Experiment Video

Updated: Jan 10, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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An EEG-sEMG Asynchronous Time-Frequency Progressive Fusion Model for Hand Trajectory Estimation.

Shengcai Duan, Le Wu, Aiping Liu

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

    This study introduces AtpFusion, a novel model for decoding 3D hand trajectories using asynchronous electroencephalography (EEG) and surface electromyography (sEMG) signals. It achieves state-of-the-art accuracy, improving motor rehabilitation and bionic device development.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Signal Processing

    Background:

    • Accurate motor trajectory estimation from physiological signals is crucial for advanced motor rehabilitation and bionic devices.
    • Existing electroencephalography (EEG) and surface electromyography (sEMG) fusion methods often assume temporal alignment, overlooking signal latency and degrading continuous motion decoding performance.

    Purpose of the Study:

    • To propose AtpFusion, an asynchronous time-frequency progressive fusion model for enhanced 3D hand trajectory decoding using EEG and sEMG.
    • To address semantic misalignment and insufficient consistency representation in current multimodal fusion techniques.

    Main Methods:

    • Developed asynchronous time-frequency inputs using a long-short time window segmentation strategy for semantic alignment.
    • Utilized long-window frequency-domain EEG (amplitude/phase) and short-window time-domain sEMG signals.
    • Implemented a progressive hierarchical fusion architecture with intra-modal and inter-modal branches for feature refinement and integration.

    Main Results:

    • Achieved state-of-the-art accuracy on the WAY-EEG-GAL dataset for continuous hand trajectory estimation.
    • Obtained a Pearson Correlation Coefficient (PCC) of 0.9278 and a Root Mean Square Error (RMSE) of 0.0916.
    • Significantly outperformed existing EEG-sEMG fusion approaches for continuous motion decoding.

    Conclusions:

    • AtpFusion presents a novel asynchronous EEG-sEMG fusion framework for high-performance multimodal bionic interfaces.
    • The model offers a significant advancement in decoding continuous motor trajectories, paving the way for more sophisticated rehabilitation and prosthetic technologies.