Electrophysiological Representations of Multivariate Human Emotion Experience
Electrophysiological Representations of Multivariate Human Emotion Experience
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DOI：
10.1080/02699931.2023.2297272
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Jin Liu;Xin Hu;Xinke Shen;Sen Song;Dan Zhang
中科院分区：
文献类型：
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作者：
Jin Liu;Xin Hu;Xinke Shen;Sen Song;Dan Zhang
尽管人类的日常情绪在本质上是共同发生的，但大多数神经科学研究主要采用单变量方法来识别情绪的神经表征（单一情绪类别内的情绪体验），而没有充分考虑不同情绪的共同发生（同时跨不同情绪类别的情绪体验）。本研究采用情景表征相似性分析（RSA）方法，探讨了多元情绪体验的神经表征。研究人员使用了78名参与者的EEG数据集，这些参与者观看了28个视频片段，并根据8种情绪类别对他们的体验进行了评分。基于EEG的电生理表征被提取为五个频带中每个通道的功率谱密度（PSD）特征。间的情况RSA方法揭示了显着的相关性之间的多元情绪体验评级和PSD功能的Alpha和Beta波段，主要是在额叶和顶叶枕叶脑区。该研究发现，在足够的情况和参与者下，所识别的EEG表示是可靠的。此外，通过一系列的消融分析，跨情境RSA进一步证明了多元情绪体验的脑电表征的稳定性和特异性。这些发现强调了采用多元观点全面理解人类情感体验的神经表征的重要性。
Despite the fact that human daily emotions are co-occurring by nature, most neuroscience studies have primarily adopted a univariate approach to identify the neural representation of emotion (emotion experience within a single emotion category) without adequate consideration to the co-occurrence of different emotions (emotion experience across different emotion categories simultaneously). To investigate the neural representations of multivariate emotion experience, this study employed the inter-situation representational similarity analysis (RSA) method. Researchers used an EEG dataset of 78 participants who watched 28 video clips and rated their experience on eight emotion categories. The EEG-based electrophysiological representation was extracted as the power spectral density (PSD) feature per channel in the five frequency bands. The inter-situation RSA method revealed significant correlations between the multivariate emotion experience ratings and PSD features in the Alpha and Beta bands, primarily over the frontal and parietal-occipital brain regions. The study found the identified EEG representations to be reliable with sufficient situations and participants. Moreover, through a series of ablation analyses, the inter-situation RSA further demonstrated the stability and specificity of the EEG representations for multivariate emotion experience. These findings highlight the importance of adopting a multivariate perspective for a comprehensive understanding of neural representation of human emotion experience.