Toward Emotion-Aware AI Agents: A General Framework for Real-Time EEG-Driven Human–Agent Collaboration
Z. Wei (TU Delft - Industrial Design Engineering)
R.S.K. Chandrasegaran – Graduation committee member (TU Delft - Industrial Design Engineering)
P.(Pan) Wang – Mentor (TU Delft - Industrial Design Engineering)
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Abstract
Generative AI reshapes creative workflows through mixed-initiative collaboration, requiring users to rapidly make decisions among alternatives and fluidly shift roles as creators and critics. During exploration, users often rely on emotional intuition for swift judgments. However, most current systems depend on explicit prompts, limiting their adaptivity across iterations. Meanwhile, EEG-based neuroadaptive systems dynamically adjust interactions, improving human–machine alignment. EmotivChat addresses this gap by continuously monitoring users’ immediate emotional responses. Within a closed-loop architecture, the system optimizes prompting strategies across rounds. The system was evaluated across two paired studies on image co-creation(N=31) and text ideation (N=29) each contrasting the EEG-adaptive system with a rigorously matched non-adaptive baseline. Results indicate EmotivChat improved perceived teaming and iteration efficiency in image co-creation, while in text ideation, it improved perceived collaboration but produced limited changes in idea outcomes. These findings validate real-time emotional feedback in LLM-based interactions, pointing the way toward cross-modal, emotion-adaptive agents.