基于脑电网络的情绪识别研究进展

Emotion Recognition Based on EEG Networks: Progress and Prospects

  • 摘要: 情绪识别使计算机系统具备感知人类情感的能力,它已成为计算机科学、心理学、社会学、生物医学工程等多个学科的研究热点。脑电(Electroencephalography, EEG)网络分析方法是神经影像领域常用的神经认知分析方法,它通过捕获脑区间交互关系构建脑网络,以此描述大脑不同区域的信息流动状态和功能协作状况。由于情绪功能本身涉及多脑区协作,脑电网络的分析方法凭借其出色的脑区信息交互捕获能力在情感识别领域发挥重要作用。该文对脑电网络情绪识别的研究背景、原理方法和研究现状进行详细介绍,并讨论了基于脑电网络分析的情绪识别研究目前存在的问题和未来发展趋势。

     

    Abstract: Emotion recognition, endowing computers with the ability to perceive emotions, is a focal point of interest in various fields, including computer science, psychology, sociology, biomedical engineering, and more. EEG network analysis methods are widely used in the neuroimaging field for neurocognitive analysis. These methods capture interactions between/among different brain regions to construct brain networks, thereby describing the information flow and functional collaboration across various brain areas. Given that emotional functions inherently involve the cooperation of multiple brain regions, EEG network analysis methods excel in capturing inter-regional information interactions, making them highly effective in emotion recognition. This paper provides a comprehensive introduction to the research background, principles, methods, and current status of EEG network-based emotion recognition. Additionally, the existing challenges and future development trends in this research area are discussed.

     

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