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Related Experiment Video

Updated: Dec 30, 2025

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A visual-haptic neurofeedback training improves sensorimotor cortical activations and BCI performance.

Zhongpeng Wang, Yijie Zhou, Long Chen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary

    This study introduces a novel visual-haptic neurofeedback training (NFT) method using brain-computer interface (BCI) technology. The approach significantly enhanced sensorimotor cortical activity and improved BCI performance during motor training.

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    Area of Science:

    • Neuroscience
    • Rehabilitation Engineering
    • Brain-Computer Interfaces

    Background:

    • Neurofeedback training (NFT) shows potential for restoring impaired brain function and neuroplasticity.
    • The specific contributions of different feedback modalities to NFT effectiveness, particularly in enhancing cortical activation for motor training, require further investigation.

    Purpose of the Study:

    • To investigate the impact of a novel brain-computer interface (BCI)-based visual-haptic neurofeedback training (NFT) on sensorimotor cortical activations and motor training performance.
    • To evaluate the efficacy of synchronous visual scene and proprioceptive electrical stimulation feedback within an NFT paradigm.

    Main Methods:

    • Development and implementation of a BCI-based visual-haptic NFT system.
    • Utilizing multi-band lateralized relative event-related desynchronization (lrERD) patterns (alpha_1, alpha_2, beta_1, beta_2 bands) to measure cortical activations.
    • Comparison of cortical activation patterns and BCI classification performance between pre- and post-NFT sessions.

    Main Results:

    • Significant enhancement in multi-band lrERD patterns, indicating improved sensorimotor cortical activations after visual-haptic NFT.
    • A substantial improvement in BCI classification performance, with an approximate 9% increase, reaching a mean accuracy of ~85%.
    • Demonstrated enhancement from a baseline of lower motor imagery BCI performance.

    Conclusions:

    • The proposed visual-haptic NFT approach is feasible and effective in enhancing sensorimotor cortical activations.
    • This NFT paradigm shows significant potential for improving BCI performance during motor training.
    • The findings support the use of integrated visual and haptic feedback for neurorehabilitation and BCI applications.