Home-based music therapy devices require accessible and cost-effective solutions for users to understand and track their therapeutic progress. Traditional physiological signal analysis, particularly EEG interpretation, relies heavily on domain experts, creating barriers to scalability and home adoption. Meanwhile, few experts are capable of interpreting physiological signal data while also making targeted music recommendations. While large language models (LLMs) have shown promise in various domains, their application to automated physiological report generation for music therapy represents an unexplored task. We present a prototype system that leverages LLMs to bridge this gap -- transforming raw EEG and cardiovascular data into human-readable therapeutic reports and personalized music recommendations. Unlike prior work focusing on real-time physiological adaptation during listening, our approach emphasizes post-session analysis and interpretable reporting, enabling non-expert users to comprehend their psychophysiological states and track therapeutic outcomes over time. By integrating signal processing modules with LLM-based reasoning agents, the system provides a practical and low-cost solution for short-term progress monitoring in home music therapy contexts. This work demonstrates the feasibility of applying LLMs to a novel task -- democratizing access to physiology-driven music therapy through automated, interpretable reporting.

翻译：居家音乐治疗设备需要为使用者提供可及且经济的解决方案，以理解和追踪其治疗进展。传统生理信号分析，尤其是脑电图解读，高度依赖领域专家，这阻碍了系统的可扩展性及家庭推广。同时，能够解读生理信号数据并针对性进行音乐推荐的专家极为稀缺。尽管大语言模型在多个领域展现出潜力，但其在音乐治疗中用于自动生成生理报告的应用仍属未探索领域。我们提出一个原型系统，利用大语言模型弥合这一差距——将原始脑电图与心血管数据转化为人类可读的治疗报告及个性化音乐推荐。与先前聚焦于聆听过程中实时生理适配的研究不同，我们的方法强调会话后分析与可解读性报告，使非专业用户能够理解其心理生理状态并长期追踪治疗成果。通过将信号处理模块与大语言模型推理代理相集成，该系统为居家音乐治疗环境中的短期进展监测提供了一种实用且低成本的解决方案。本研究证实了大语言模型在全新任务上的可行性——通过自动化、可解读的报告，推动生理驱动型音乐治疗的普及化。