How does Music Affect Your Brain? A Pilot Study on EEG and Music Features for Automatic Analysis

Area of Science:

• Neuroscience and Computational Musicology
• Investigates the neural and computational underpinnings of music-induced emotional responses.

Background:

• Music effectively modulates emotions and is used in clinical settings.
• Existing emotion prediction methods are complex, time-consuming, and expensive.
• Electroencephalogram (EEG) provides objective physiological data on music's impact.

Purpose of the Study:

• To develop an efficient and accurate method for predicting music-induced emotions.
• To fuse electroencephalogram (EEG) and music features for enhanced prediction.
• To explore the relationship between specific music features and EEG patterns.

Main Methods:

• Proposed a novel method utilizing a grey wolf optimizer algorithm.
• Integrated electroencephalogram (EEG) features with music features for analysis.
• Compared the proposed method against alternative approaches for performance evaluation.

Main Results:

• The grey wolf optimizer-based method achieved promising performance in predicting emotional responses to music.
• The proposed method outperformed alternative existing methods.
• Identified significant correlations between musical timbre features and EEG features.

Conclusions:

• The developed method offers an efficient and potentially cost-effective approach for automatic emotion prediction from music.
• Findings provide a basis for extending music's application in therapeutic and other contexts.
• Highlights the importance of musical timbre in eliciting specific electroencephalogram responses.