MindMelody: A Closed-Loop EEG-Driven System for Personalized Music Intervention
Yimeng Zhang, Yueru Sun, Haoyu Gu, Zhanpeng Jin

TL;DR
MindMelody is a real-time, closed-loop system that uses EEG signals to generate personalized music interventions, adapting dynamically to users' emotional states for improved emotion regulation.
Contribution
It introduces a novel EEG-to-music framework with an emotion-mediated semantic bridge and a feedback loop for real-time personalized music generation.
Findings
Enhances control adherence and emotional alignment in music generation.
Receives higher perceived helpfulness in short-term listening tests.
Demonstrates promise as an adaptive affect-aware music system.
Abstract
Driven by the escalating global burden of mental health conditions, music-based interventions have attracted significant attention as a non-invasive, cost-effective modality for emotion regulation and psychological stress relief. However, current digital music services rely on static preferences and fail to adapt to users' instantaneous psychological states. Furthermore, directly mapping electroencephalography (EEG) to music generation remains challenging due to severe paired-data scarcity and a lack of interpretability. To address these limitations, we propose MindMelody, a fully functional, closed-loop real-time system for EEG-driven personalized music intervention. MindMelody introduces an emotion-mediated semantic bridge. Specifically, a hybrid Transformer-GNN first decodes real-time EEG signals into global Valence-Arousal states and local temporal affect trajectories. These states…
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