Music-emotion EEG coupling effects based on representational similarity

Jiayang Xu; Liangliang Hu; Rui Qiao; Yilin Hu; Yin Tian

doi:10.1016/j.jneumeth.2023.109959

Music-emotion EEG coupling effects based on representational similarity

J Neurosci Methods. 2023 Oct 1:398:109959. doi: 10.1016/j.jneumeth.2023.109959. Epub 2023 Sep 1.

Authors

Jiayang Xu¹, Liangliang Hu², Rui Qiao¹, Yilin Hu¹, Yin Tian³

Affiliations

¹ School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
² School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; West China institute of children's brain and cognition, Chongqing university of education, Chongqing 400065, China.
³ School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Institute for Advanced Sciences，Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing 400064, China. Electronic address: tianyin@cqupt.edu.cn.

PMID: 37661055
DOI: 10.1016/j.jneumeth.2023.109959

Abstract

Background: Music can evoke intense emotions and music emotion is a complex cognitive process. However, we know little about the cognitive mechanisms underlying these processes, and there are significant individual differences in the emotional responses to the same musical stimuli.

New method: We used the inter-subject representational similarity analysis (IS-RSA) method to investigate the shared music emotion responses across multiple participants. In addition, we extended IS-RSA to estimate the group cross-frequency coupling effects of music emotion. Based on the cross-frequency coupling IS-RSA, we analyzed the differences in cross-frequency coupling patterns under different music emotions using MI. Comparison of existing methods: most current IS-RSA analyses focus on within-frequency band analysis. However, the cognitive processing of music emotion involves not only activation and brain network connections differences within frequency bands but also information communication between frequency bands.

Results: The results of the within-frequency band IS-RSA analysis showed that the theta and gamma frequency bands play important roles in the inter-participant consistency of music emotion. The inter-frequency band IS-RSA analysis showed that the theta-beta coupling pattern exhibited stronger inter-participant consistency compared to the theta-gamma coupling pattern, and the theta-beta coupling had significant consistent representation across various music conditions. Through the significant regions of cross-frequency coupling representation similarity analysis, we performed phase-amplitude coupling analysis on FC4-C6 and FC4-Pz connections. For the theta-beta coupling pattern, we found that the MI of these two connections exhibited different coupling patterns under different music conditions, and they showed a significant decrease compared to the baseline period.

Keywords: EEG; Emotion; Inter-subject representational similarity; Music; Phase-amplitude coupling.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Auditory Perception / physiology
Brain / physiology
Electroencephalography / methods
Emotions / physiology
Humans
Music* / psychology