Real-Time BCI-Generated Ethnic-Inspired Music Based on Orff Principles: Effects on Relaxation and Potential Support for Language Development in Children with Autism Spectrum Disorder

This study presents a real-time brain-computer interface (BCI) framework for generating culturally resonant music from neural oscillations, tailored for potential therapeutic applications in children with autism spectrum disorder (ASD). An optimized embedded Runge-Kutta 7(6) pair was developed to efficiently integrate Jansen-Rit neural mass models, achieving approximately 50% fewer function evaluations and latencies below 50 ms compared to standard benchmarks, enabling smooth performance on consumer-grade hardware. Using publicly available resting-state EEG datasets and model simulations, band-specific power estimates (alpha, theta, beta) were mapped to musical parameters inspired by Orff-Schulwerk principles and Chinese ethnic minority children's songs: pentatonic scales, repetitive cyclic phrases, isochronous rhythms (60–90 bpm modulated by alpha power), and prosodic arcs shaped by theta dynamics. The generated melodies exhibited strong perceptual fidelity to reference folk excerpts, as quantified by reduced mel-frequency cepstral coefficients (MFCC) distances. The platform's emphasis on predictable, low-arousal, and speech-like structures aligns with evidence-based music therapy mechanisms for promoting relaxation (enhanced alpha activity) and supporting communicative development in ASD. While evaluations were limited to simulations and adult resting-state data, the reproducible design establishes technical feasibility and cultural relevance, laying groundwork for future studies examining relaxation and language-supportive applications in pediatric ASD populations.