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Related Experiment Video

Updated: Sep 12, 2025

Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients
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DualDyConvNet: Dual-Stream Dynamic Convolution Network via Parameter-Efficient Fine-Tuning for Predicting Motor

Yunjeong Jang, Joohye Jeong, Yun Kwan Kim

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |August 4, 2025
    PubMed
    Summary
    This summary is machine-generated.

    A new DualDyConvNet framework accurately predicts motor recovery in subacute stroke patients using resting-state EEG data. This aids personalized rehabilitation planning and improves patient quality of life.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Rehabilitation Medicine

    Background:

    • Stroke significantly impacts global health, affecting motor function recovery.
    • Predicting early motor recovery potential is crucial for personalized stroke rehabilitation.
    • Current methods for predicting motor prognosis have limited performance.

    Purpose of the Study:

    • To propose a novel DualDyConvNet framework for predicting motor recovery in subacute stroke patients.
    • To utilize resting-state electroencephalogram (EEG) data for motor prognosis prediction.
    • To enhance the accuracy and generalizability of motor recovery prediction models.

    Main Methods:

    • Developed a dual-stream dynamic convolution network (DualDyConvNet) with channel and spatial streams.
    • Employed resting-state EEG data from subacute stroke patients (SMC and KIST datasets).
    • Quantified motor recovery using the Fugl-Meyer Assessment of the upper limb.

    Main Results:

    • Achieved low root mean squared errors (RMSE) on internal validation: 0.070 ± 0.045 (SMC) and 0.223 ± 0.148 (KIST).
    • Demonstrated superior performance compared to existing models.
    • Showcased strong generalization through cross-dataset validation with and without Euclidean-space alignment (EA).

    Conclusions:

    • The DualDyConvNet framework effectively predicts motor function prognosis in stroke patients.
    • This prediction capability can facilitate early, personalized rehabilitation planning.
    • Improved prognostication has the potential to enhance the quality of life for stroke survivors.