Democratizing Music Therapy: LLM-Based Automated EEG Analysis and Progress Tracking for Low-Cost Home Devices

2026-01-18

AI Summary

This study addresses the lack of low-cost, interpretable physiological signal analysis tools that hinder non-specialist users from tracking therapeutic efficacy in home-based music therapy. To bridge this gap, we propose the first framework leveraging large language models (LLMs) for generating physiology-driven music therapy reports. By integrating EEG and cardiovascular signal processing modules with an LLM-based reasoning agent, our approach automatically produces natural-language therapy summaries and personalized music recommendations—eliminating the need for expert intervention. The system establishes an end-to-end pipeline that transforms raw physiological data into interpretable, actionable feedback, thereby demonstrating the feasibility of combining LLMs with physiological signal interpretation for music-based interventions. This work offers a practical, scalable, and cost-effective solution to support accessible home music therapy.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Brain-Sensing and Analysis

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• music therapy
• EEG analysis
• home-based devices
• progress tracking
• physiological signals

Innovation

• LLM-based EEG analysis
• automated therapeutic reporting
• personalized music recommendation
• home-based music therapy
• interpretable physiological feedback