# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 1 Introduction  
### Background and Goals of Study  
The rapid advancement of artificial intelligence (AI) has driven significant innovation across multiple disciplines, including information technology, medicine, education, and the social sciences. In particular, AI has played a pivotal role in the evolution of affective computing, enabling the quantitative analysis and interpretation of human emotions through computational models [1, 2]. Recently, the emergence of brain-computer interface (BCI) technology has garnered increasing attention, with electroencephalography (EEG)-based emotion recognition emerging as a promising tool for real-time affective state detection [3]. EEG captures the brain's electrical activity, facilitating the establishment of correlations between neural signals and specific emotional states. This capability has opened new avenues for real-time emotion detection and adaptive system development, with applications spanning healthcare, human-computer interaction (HCI), and brain-computer interfacing [4, 5]. Despite these advancements, the application of emotion recognition technology in architectural environments remains largely unexplored. Traditional methods for analysinghuman affective responses to built environments have primarily relied on subjective techniques such as surveys, psychological interviews, and behavioural observations [6]. While these approaches provide valuable insights into emotional experiences, they are inherently limited by subjectivity, response biases, and an inability to capture real-time emotional fluctuations. As a result, the integration of objective biometric data, such as EEG signals, with AI-driven emotion analysis represents a promising direction for enhancing user experiences in adaptive architectural environments [7; 8]. Recent studies have demonstrated the feasibility of AI in EEG-based emotion recognition, paving the way for its application in various domains. For instance, a 2023 study conducted at the University of California, Berkeley, successfully analysed EEG data from epilepsy patients to reconstruct the song Another Brick in the Wall by Pink Floyd, highlighting the potential of AI in decoding human thoughts and emotions through EEG signals [9]. However, while these findings underscore the potential of AI-driven emotion analysis, their application to real-time, adaptive architectural settings remains underdeveloped. Existing research has primarily focused on HCI and medical applications, leaving a critical gap in understanding how real-time affective computing can be integrated into architectural design [10; 11]. To address this gap, this study aims to develop an AI model capable of real-time EEG-based emotion recognition and its application to adaptive architectural environments. Specifically, this research investigates the feasibility of sensing architecture, in which built environments dynamically adjust in response to users' emotional states, captured via biometric feedback. The study focuses on training and evaluating an EEG-based AI model for three-class emotion classification--positive, neutral, and negative--and implementing it within a 360\({}^{\circ}\) virtual reality (VR) environment to assess its effectiveness in adaptive architectural design [12]. This research distinguishes itself from prior studies by shifting the focus of emotion recognition from conventional static methods to real-time biometric analysis within architectural spaces. While previous work has predominantly relied on self-reported emotional assessments and observational studies, this study explores how EEG-based AI models can dynamically detect and respond to users' emotional states in real-time. Additionally, whereas prior studies have focused on HCI and medical contexts, this study investigates the implications of EEG-based emotion recognition for user-centred architectural design, emphasising its potential for enhancing occupant experience, comfort, and engagement [13; 14].