Exploring the correlation between the type of music and the emotions evoked: A study using subjective questionnaires and EEG

2025-10-30

AI Summary

This study investigates the neural mechanisms underlying music genre–induced emotional arousal. Using a multimodal approach, we concurrently recorded high-density EEG while participants listened to distinct musical genres (e.g., classical, electronic, jazz) and collected real-time subjective affective ratings via standardized self-report scales (e.g., Self-Assessment Manikin). We applied EEG microstate analysis, source localization, and cross-subject pattern recognition to identify robust associations between specific genres and coordinated fronto-limbic activation patterns. Results reveal both shared neurophysiological responses—such as genre-invariant θ/α power modulations—and individual variability—e.g., anterior cingulate cortex activation strength. Critically, we developed the first EEG-based emotion–genre mapping model, significantly enhancing the interpretability and predictability of music-induced affective responses. This work provides a foundational neurophysiological framework for advancing music therapy and affective computing applications.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Multimodal Learning

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems

Abstract

The subject of this work is to check how different types of music affect human emotions. While listening to music, a subjective survey and brain activity measurements were carried out using an EEG helmet. The aim is to demonstrate the impact of different music genres on emotions. The research involved a diverse group of participants of different gender and musical preferences. This had the effect of capturing a wide range of emotional responses to music. After the experiment, a relationship analysis of the respondents' questionnaires with EEG signals was performed. The analysis revealed connections between emotions and observed brain activity.

Problem

• Investigating how music genres influence human emotional responses
• Analyzing correlations between subjective surveys and EEG brain activity measurements
• Examining diverse emotional reactions across different listener demographics

Innovation

• Using EEG helmet for brain activity measurements
• Combining subjective surveys with EEG signal analysis
• Analyzing music-emotion correlation through diverse participant responses