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CGNet:一个复杂值的图形网络,用于在基于EEG的大脑与计算机接口中共同学习振幅相信息.

Guoqing Cai¹, Yiyi Chen², Bolun Yang¹

¹School of Information Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.

Neural networks : the official journal of the International Neural Network Society

|July 11, 2025

概括

本研究介绍了用于脑电图 (EEG) 大脑计算机接口 (BCI) 的复杂值图形网络 (CGNet). CGNet有效地整合了振幅和相位信息,大大提高了运动任务的神经解码精度.

关键词:

广度阶段学习的学习.大脑计算机接口复杂值计算的计算方法全球依赖性全球依赖性图表卷积网络卷积网络

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科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
信号处理信号处理

背景情况:

基于脑电图 (EEG) 的脑电脑接口 (BCI) 对于理解神经过程至关重要.
当前的深度学习方法经常独立处理EEG振幅和相位,错过了它们的协同作用.

研究的目的:

开发一种新的深度学习模型,以捕捉EEG信号中的振幅和相位的组合信息.
通过利用神经信号振幅和相位之间的相互作用来提高基于EEG的BCI的性能.

主要方法:

构建一个复杂值图形网络 (CGNet) 来编码振幅和阶段到一个复杂值表示.
采用两级复杂值卷积网络,空间注意力和动态图形卷积来进行时空特征提取.
将CGNet扩展为波段CGNet (FBCGNet),以提高对宽带EEG数据的适应性.

主要成果:

CGNet在运动图像和执行BCI任务方面实现了最先进的分类性能.
与CGNet相比,FBCGNet表现出了进一步的性能改善.
视觉化证实了CGNet能够识别与BCI范式相关的关键时空信息的能力.

结论:

CGNet有效地挖掘振幅相信息,在基于EEG的BCI中提供更全面的神经解码.
拟议的模型优于单独使用振幅或相位的方法.
CGNet代表了提高BCI准确性和效率的重大进步.