Emotion Analysis AI Model for Sensing Architecture Using EEG
Source abstract
Abstract
The rapid advancement of artificial intelligence (AI) has spurred innovation across various domains—information technology, medicine, education, and the social sciences—and is likewise creating new opportunities in architecture for understanding human–environment interactions. This study aims to develop a fine-tuned AI model that leverages electroencephalography (EEG) data to analyse users’ emotional states in real time and apply these insights to architectural spaces. Specifically, the SEED dataset—an EEG-based emotion recognition resource provided by the BCMI laboratory at Shanghai Jiao Tong University—was employed to fine-tune the ChatGPT model for classifying three emotional states (positive, neutral, and negative). Experimental results demonstrate the model’s effectiveness in differentiating these states based on EEG signals, although the limited number of participants confines our findings to a proof of concept. Furthermore, to assess the feasibility of the proposed approach in real architectural contexts, we integrated the model into a 360° virtual reality (VR) setting, where it showed promise for real-time emotion recognition and adaptive design. By combining AI-driven biometric data analysis with user-centred architectural design, this study aims to foster sustainable built environments that respond dynamically to human emotions. The results underscore the potential of EEG-based emotion recognition for enhancing occupant experiences and provide foundational insights for future investigations into human–space interactions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Emotion and Mood Recognition · EEG and Brain-Computer Interfaces · Color perception and design
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Architecture, Hanyang University, Seoul 04763, Republic of Korea
Funding
National Research Foundation of Korea (NRF)
References (46)
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