Electrophysiological Representations of Multivariate Human Emotion Experience

Abstract

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. The researchers used an EEG dataset of 78 participants who watched 28 video clips (emotional situations) and rated their experience on eight emotion categories: anger, disgust, fear, sadness, amusement, inspiration, joy, and tenderness. The EEG-based electrophysiological representation was extracted as the power spectral density (PSD) feature per channel in the five frequency bands (Delta, Theta, Alpha, Beta, and Gamma). 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, which were different from those univariate-based findings. These findings highlight the importance of adopting a multivariate perspective for a comprehensive understanding of the neural representation of human emotion experience.