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基于源空间中的EEG-fNIRS混合大脑网络的情绪识别

Mingxing Hou^1,2, Xueying Zhang³, Guijun Chen³

¹College of Integrated Circuits, Taiyuan University of Technology, Taiyuan 030600, China.

|January 8, 2025

概括

这项研究引入了使用脑电图 (EEG) 和功能近红外光谱 (fNIRS) 的混合脑网络,以改善情绪识别. 这种新的方法通过分析源空间中的因果和合的大脑活动来提高准确性.

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:

像EEG和fNIRS这样的多模式生理信号比单模式方法提高了情绪识别.
关于EEG和fNIRS在情绪识别中用于大脑网络构建的关系的研究有限.

研究的目的:

开发一种使用同步EEG-fNIRS信号构建混合大脑网络的新方法.
通过整合因果和合大脑网络来提高情绪识别性能.

主要方法:

对EEG信号的源部位定位,以导出源信号.
格兰杰对EEG源信号的因果关系分析,以建立因果大脑网络.
对结合的大脑网络的EEG和fNIRS信号之间的合强度评估.
将因果和合网络集成到混合大脑网络中,以提取特征.

主要成果:

拟议的混合大脑网络方法在多个情绪数据集上得到了验证.
实验结果表明,与基线方法相比,情绪识别性能明显优越.

结论:

这项工作提供了关于融合EEG和fNIRS信号以进行情绪识别的新观点.
这项研究提供了一个可行的解决方案,通过混合大脑网络建设来提高情感识别的准确性.

关键词:

欧洲电力联盟 (EEG) 在尼尔斯.大脑网络大脑网络情感识别情感识别情感识别源空间是源空间的空间.

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