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MSDAC:一个多源域对抗框架,用于在皮层内脑-计算机接口中的运动预测.

Haozhou Liu, Banghua Yang, Shouliang Guan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 3, 2025
    PubMed
    概括

    新的脑电脑界面解码方法改善了患者的稳定性. 多源域对抗分类 (MSDAC) 框架提高了跨日解码精度,而不需要重新校准.

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    科学领域:

    • 神经科学是一个神经科学.
    • 生物医学工程 生物医学工程
    • 机器学习 机器学习

    背景情况:

    • 皮层内脑-计算机接口 (iBCI) 能够在中恢复运动功能.
    • 目前的iBCI解码方法需要经常重新校准,因为神经数据不稳定,阻碍了可靠的在线控制.

    研究的目的:

    • 为iBCI开发一个强大的跨日解码框架,尽量减少频繁重新校准的需要.
    • 通过解决神经数据变异性来提高iBCI系统的稳定性和性能.

    主要方法:

    • 提出了一个多源域对抗分类 (MSDAC) 框架,利用分布外泛化.
    • 实施对抗性网络以最大限度地减少跨历史数据域 (按日期) 的分布差异.
    • 评估了MSDAC框架的五个月子中心外神经活动数据.

    主要成果:

    • 在不使用测试日数据进行校准的情况下,MSDAC框架在150天内实现了平均84.38%的解码精度.
    • 证明了强大的域不变特征,导致在未见测试数据上的卓越性能.
    • 显著提高解码稳定性,减少了重新校准的需要.

    结论:

    • 在iBCI系统中,MSDAC框架为跨日解码提供了稳定有效的解决方案.
    • 这种方法有可能显著提高iBCI在患者的临床适用性.
    • MSDAC代表了开发可靠,长期可靠的大脑与计算机接口的有希望的方向.