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Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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解码混合EEG-fNIRS上肢运动执行与囊动态图卷积神经网络.

Zhizheng Yuan, Yu Li, Haiyan Zhang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    PubMed
    概括

    这项研究引入了一种新的深度学习模型,EF-CapsDGCN,用于使用脑电图 (EEG) 和功能近红外光谱 (fNIRS) 信号解码上肢运动. 与单一模式方法相比,多式模式方法显著提高了准确性.

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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:

    • 解码运动执行对于脑计算机接口 (BCI) 至关重要.
    • 结合脑电图 (EEG) 和功能近红外光谱 (fNIRS) 提供了用于增强BCI性能的补充信息.
    • 现有的方法往往与有效的多式联络融合作斗争.

    研究的目的:

    • 提出和评估一种新的囊动态图形卷积网络 (EF-CapsDGCN),用于精确解码上肢运动执行.
    • 使用拟议的EF-CapsDGCN模型研究融合EEG和fNIRS信号的有效性.
    • 将EF-CapsDGCN的性能与最先进的方法和单模式方法进行比较.

    主要方法:

    • 使用共享的卷积架构从EEG和fNIRS信号中提取特征.
    • 功能向囊的动态路由和多式联运囊的形成.
    • 通过在囊节点上的动态图形卷积来学习隐藏的表示.
    • 在分类之前,用于功能集成的多头自我注意机制.

    主要成果:

    • 拟议的EF-CapsDGCN在HYGRIP多式联络数据集上实现了优异的分类性能.
    • 使用EF-CapsDGCN的多模式EEG-fNIRS融合,与单一模式相比,分类准确度至少增加了8%.
    • 该模型显示了与ANN,DeepConvNet,DNN和EF-Net.Net等现有方法相比的显著改进.

    结论:

    • 囊动态图形卷积对于EEG和fNIRS信号的多式融合是有效的.
    • 在基于EEG-fNIRS的BCI中,EF-CapsDGCN模型对精确的运动执行解码非常有前途.
    • 这项研究为多式联接BCI解码提供了有效的解决方案,对运动障碍康复具有临床意义.