NeuroPrompt: EEG-Conditioned Emotion-Aware Dialogue Generation with Large Language Models

Although large language models can generate fluent dialogue, emotional response generation often relies on explicit emotion cues or manual labels that are rarely available in realistic interaction. Since EEG signals have been widely studied as a physiological source for emotion recognition, they can serve as an implicit affect cue for language models. To address the lack of paired EEG-dialogue corpora, we propose a staged framework for EEG-conditioned dialogue generation. This framework learns cross-subject emotion EEG representations, maps them into the LLM embedding space, and uses the projected representation to guide emotion-adaptive response generation. Under a cross-subject protocol, blind GPT-5.4 evaluation on 1,598 paired dialogue samples shows that EEG-Conditioned LLM achieves a 31.93% win rate and 2.23 average rank, outperforming Text-Only Baseline (22.44%/2.45) and Unconditioned LLM (11.67%/3.17) while approaching Gold-Label Oracle (33.96%/2.14); a 500-sample human questionnaire shows the same trend. We further conduct an online real-time EEG user study in which each participant's EEG is fed to the model during live interaction, and anonymized responses are rated for how well they match the participant's current emotional state. In this online study, EEG-Conditioned LLM also achieves the best human ratings and average rank among the compared models. Taken together, the offline and online results support EEG as a practical source of implicit emotion cues for response adaptation and suggest that brain-derived signals can improve dialogue generation beyond text-only supervision.