通过深度学习进行大脑分析,以接近人类肌肉协同作用
Elham Samadi1, Fereidoun Nowshiravan Rahatabad1, Ali Motie Nasrabadi2
1Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Cognitive neurodynamics
|February 25, 2025
概括
这项研究结合了电脑电图 (EEG) 和电肌图 (EMG) 信号,使用图形理论来分析手部运动期间的肌肉-大脑协同作用. 这种新的方法显著改善了肌肉和大脑信号的协同分析.
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
- 信号处理 信号处理
背景情况:
- 脑电图 (EEG) 和肌电图 (EMG) 信号对于分析大脑和肌肉活动至关重要.
- 脑电图和电磁图信号中的噪音,例如电肌图波,往往会阻碍准确的分析.
- 结合EEG和EMG信号,可以改善肌肉运动和神经连接的协同分析.
研究的目的:
- 通过使用图形理论来研究手部运动期间EMG和EEG信号之间的协同相互作用.
- 开发一种方法来从大脑连接模式中重建肌肉信号.
- 为了提高肌肉-大脑协同分析的准确性.
主要方法:
- 从EEG和EMG信号中消除噪音.
- 对EEG数据进行图形特征分析,绘制大脑连接的地图.
- 使用两个神经网络方法计算协同效应:使用神经网络和卷积网络进行回归分析.
- 从大脑连接图中重建肌肉信号.
主要成果:
- 在估计和实际协同EMG信号之间达到99.8%的高相关性值.
- 证明了最大的平均平方误差 (MSE) 为0.0084.
- 拟议的基于图形的回归分析方法与现有的基于图形的技术相比,显示出明显优异的性能.
结论:
- 开发的方法通过分析EEG和EMG信号之间的相互作用来有效估计肌肉-大脑协同作用.
- 这种方法对推进康复策略和大脑与计算机接口充满希望.
- 图形理论和神经网络的整合为协同信号分析提供了一个强大的框架.
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