FMEFF Mechanism: A FastDTW-Based Music-EEG Feature Fusion Approach for Identifying Enjoyment Levels in Music Therapy

Research indicates that individuals who experience greater music enjoyment exhibit stronger brain–music synchronization, which is positively correlated with improved therapeutic outcomes in depression treatment. However, most existing studies on music therapy predominantly focus on emotional states, often overlooking enjoyment levels. To address this gap, we propose a novel fusion mechanism based on Fast Dynamic Time Warping (FastDTW) to objectively assess music enjoyment. First, we introduce an Adaptive Dynamic Search (ADS) algorithm that optimizes the order of Mel-Frequency Cepstral Coefficients (MFCCs) extracted from electroencephalogram (EEG) signals. This method strikes a balance between feature accuracy and redundancy, outperforming conventional fixed-order approaches. Subsequently, we analyze synchronization using the FastDTW-based Music–EEG Feature Fusion (FMEFF) framework, which leverages FastDTW to align music and EEG feature dimensions, effectively identifying the optimal alignment path to characterize synchronization. The fused features are then employed to classify music enjoyment levels, with explainability enhanced by examining classification performance across specific brain regions. Experimental results demonstrate enjoyment-level recognition accuracies of 74.50% and 81.54% in healthy and depressed participants, respectively, with the frontal lobe yielding the highest accuracy. Further correlation analysis confirms a strong relationship between the extracted features and subjective enjoyment scores. These findings present new insights for personalized music therapy recommendations and highlight the promise of integrating music and EEG signals in affective computing. This work also contributes to EEG channel optimization strategies and lays a foundation for future mental health interventions using wearable EEG systems.