# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 5 Conclusions and Future Research  
This study developed and tested a fine-tuned AI model utilising the GPT architecture to analyse emotional states from EEG (electroencephalography) data. As GPT models were originally designed for processing text-based, non-numeric data, a data-structuring process incorporating vectorisation and pattern mapping was employed to effectively handle EEG signals. The integration of EEG data into this framework enabled real-time emotion classification without extensive preprocessing, demonstrating the feasibility of LLM-based inference for EEG-based affective computing. These findings present a novel approach to applying AI-driven emotion recognition in architectural environments, offering new possibilities for emotion-adaptive spatial configurations. Furthermore, this study validated the effectiveness of EEG-based emotion recognition using the SEED dataset, successfully classifying positive, neutral, and negative emotional states. The results confirm that EEG signals contain valuable features for affective computing, contributing to advancements in brain-computer interfaces (BCIs) and human-computer interaction (HCI). However, due to the limited number of participants, these findings should be considered preliminary evidence--proof of concept for the proposed method. Future research should involve a larger and more diverse participant group, allowing for improved model robustness, generalisability, and adaptability to individual differences and real-world variations. Beyond conventional lab-based validation, this study also explored the applicability of the fine-tuned model in architectural environments using 360\({}^{\circ}\) virtual reality (VR) simulations. Architectural spaces such as Seoul's Dongdaemun Design Plaza (DDP) and New York's Long Island City were simulated to evaluate user emotional responses to different spatial settings. The results suggest that architectural elements, both natural and artificial, influence user emotions, reinforcing the importance of integrating real-time emotional metrics into user-centred architectural design. These findings support the development of emotion-aware spatial configurations, where EEG-based emotion recognition informs intelligent adaptive environments. For future research, further validation of the fine-tuned model in real-world architectural contexts will be pursued. While the SEED dataset provided a suitable foundation, additional EEG data collected within actual built environments will enhance model refinement and broaden its applicability. Furthermore, investigating alternative AI architectures beyond GPT-based models, including hybrid CNN-LSTM and LLM frameworks, could provide deeper insights into the comparative strengths and limitations of different AI-driven emotion recognition approaches. Addressing these areas will further substantiate the integration of EEG-based affective computing with adaptive, emotion-responsive architectural environments, contributing to the next generation of intelligent, human-centred spaces. Conceptualization, S.-Y.J. and H.-J.J.; Methodology, S.-Y.J. ; Software, M.-K.K. ; Validation, S.-Y.J., M.-K.K. and H.-J.J.; Formal analysis, S.-Y.J. ; Investigation, S.-Y.J. ; Resources, H.-J.J.; Data curation, M.-K.K. ; Writing--original draft preparation, S.-Y.J. ; Writing--review and editingH.-J.J.; Visualization, M.-K.K. ; Supervision, H.-J.J.; Project administration, H.-J.J.; Funding acquisition, H.-J.J. All authors have read and agreed to the published version of the manuscript. This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government. (2022R1A2C3011796). The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) of Hanyang University (Approval No. HYUIRB-202311-026, approved on 13 November 2023). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.