MindLoop: LLM-Based Closed-Loop Emotion Regulation with Wearable BCI Feedback

Emotion regulation is essential for mental health, but access to trained professional support remains limited. Large Language Models (LLMs) offer a promising way to make emotional support more accessible through conversational interaction. However, existing studies on LLM-based emotional support mainly focus on current turn response quality, while paying limited attention to the overall regulation process. Effective emotion regulation is a structured closed-loop process requiring multi-stage guidance and timely affective feedback, raising three challenges: structuring dialogue, balancing current response quality with overall regulation outcomes, and obtaining timely affective observations. We propose MindLoop, a closed-loop policy learning approach for LLM-based emotion regulation with wearable BCI feedback. MindLoop first introduces a CBT-guided stage–strategy plan to organize multi-turn dialogue into structured regulation stages and stage-specific interventions. Based on this structure, we further develop a dual-timescale reinforcement learning method to balance overall regulation progress and current-stage intervention quality. To provide objective and timely observations of affective changes, we introduce wearable EEG/fNIRS-based affect decoding as physiological feedback for adapting regulation decisions. Simulated-interaction evaluation shows strong regulation-stage progression, successful completion, and affective outcomes. A 23-participant study further provides preliminary evidence that the simulation-trained policy supports structured real-world interaction with favorable affective changes and session ratings.