Title:Predictive Analysis of the Mental Health Risks of Music by EEG and Magnetic Resonance Imaging
Volume: 18
Author(s): Jiexin Chen, Yibing Cao*Lei Xu
Affiliation:
- School of Business, Anhui Xinhua University, Hefei 230088, Anhui, China
Keywords:
Neuroplasticity analysis, electroencephalogram analysis, magnetic resonance imaging, music and mental health, self-rating, positive music.
Abstract:
Introduction: The current research on the mental health risks of music has shortcomings
in data collection, individual differences, and evaluation criteria. For this reason, this article
will use neuroplasticity EEG and magnetic resonance imaging techniques to provide a basis for
early identification and prevention of music-related mental health risks.
Methods: First, EEG was used to perform neuroimaging tests on participants, and it was observed
that music stimulation can cause specific changes in brain electrical activity, and the EEG characteristics
were preprocessed; then magnetic resonance imaging technology was used to further reveal
the structural and functional changes of the brain under music stimulation, and the potential
regulatory effects of music on mental health risks were discovered.
Results: The average Valence score of participants after playing positive music increased from 3.5
points to 7.15 points, and the degree of pleasure increased by 3.65 points (p<0.05) with statistically
significant differences; the influence of brainwave music on beta waves is also more significant
(p<0.01). Discussion: The results of this study show that music has a significant impact on mental
health. Positive music can significantly improve the pleasure of participants, while bad music may
lead to a decrease in pleasure.
Conclusion: This study demonstrates that music significantly influences brain activity and emotional
states, as evidenced by EEG and MRI data. Positive music enhances pleasure and modulates
beta waves, suggesting a protective effect on mental health, while negative music may pose emotional
risks. These neurobiological markers offer objective tools for early prediction and personalized
intervention in music-related mental health issues. Despite limitations in sample size and
short-term observation, our findings advance the use of neuroimaging in identifying at-risk individuals
and support the development of music-based preventive strategies.
