Applying TCN-Based Rhythmic Electroencephalogram Data to Assist in the Construction of Autism Intervention Pathways in Music Therapy
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Abstract
This study addresses the challenge of personalizing music therapy pathways for autism spectrum disorder (ASD) by developing a rhythmic electroencephalogram (EEG) decoding framework based on Temporal Convolutional Networks (TCN). Considering EEG as a representative bioelectromagnetic signal, the proposed approach provides an effective means for extracting neural dynamics relevant to individualized intervention assessment. Pre- and post-intervention EEG data were collected from 55 children with ASD during a 12-week music therapy program. The TCN model achieved an accuracy of 0.861 (F1 = 0.843) in classifying treatment responders, outperforming baseline models including Transformer (accuracy = 0.816). For regression, it attained an R2 of 0.692 (RMSE = 6.52) in predicting changes in the Social Responsiveness Scale (∆SRS). Responders exhibited significantly higher Beta/Gamma relative power and Theta/ Alpha-weighted Phase Lag Index (wPLI). A Neural Response Index (NRI), derived from TCN-decoded features, showed a strong correlation with ∆SRS (r = 0.712, p < 0.001). Empirical application of NRI-based decision rules yielded an 88.9% response rate in the high-NRI pathway. These findings demonstrate that TCN can effectively decode bioelectromagnetic neural patterns associated with therapeutic outcomes and that NRI provides a quantitative, data-driven indicator for personalized music therapy planning, offering methodological insights for advanced electrophysiological signal analysis and intelligent biomedical sensing applications.
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