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    Area of Science:

    • Neuroscience
    • Music Psychology
    • Machine Learning

    Background:

    • Understanding music-induced emotion is crucial for applications like affective computing.
    • Previous research often overlooks the influence of music genre on emotional prediction.
    • Advanced signal processing techniques can potentially enhance emotion recognition accuracy.

    Purpose of the Study:

    • To investigate the impact of music genre on the accuracy of music emotion prediction.
    • To compare the performance of different machine learning classifiers for music emotion analysis.
    • To evaluate the effectiveness of advanced features, such as asymmetries, in improving classification.

    Main Methods:

    • Collected electroencephalogram (EEG) data from 10 subjects listening to 20 musical pieces across 5 genres (classical, heavy metal, EDM, pop, rap).
    • Employed various machine learning classifiers and utilized a 10-fold cross-validation approach.
    • Extracted advanced features, including asymmetries, from the EEG data for enhanced analysis.

    Main Results:

    • Achieved high classification accuracies: 98.4% for subject-independent and 99.0% for subject-dependent predictions.
    • Demonstrated that pop music emotion was predicted with the highest accuracy (99.6%).
    • Identified genre as a significant factor influencing the precision of music emotion analysis.

    Conclusions:

    • Music genre plays a critical role in the accurate prediction of music-induced emotions.
    • Advanced features and robust cross-validation methods yield high classification performance.
    • The findings have implications for personalized music recommendation systems and therapeutic applications.