# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 1 Introduction  
Spending prolonged periods in indoor environments can have an effect on people's emotional stability, creativity, and stress levels, which are heavily dependent on the physical and environmental elements of architectural spaces [1, 2, 3, 4]. However, traditional design processes often rely on intuition and subjective judgments to gauge users' potential emotional experiences; thus, they cannot accurately capture unconscious and immediate emotional states [4, 5]. Methods such as interviews and surveys commonly used in prior research focus mainly on conscious and verbally expressed emotions but do not fully capture subtle, instantaneous affective responses elicited by spatial stimuli [6, 7]. To overcome these challenges, neuroscientific approaches have been recently introduced. In particular, electroencephalography (EEG) offers high temporal resolution and noninvasive measurement capabilities, enabling the real-time evaluation of users' unconscious affective and cognitive states [8, 9].  
Among the various EEG analysis techniques, event-related potential (ERP) analysis is a precise and powerful tool for measuring immediate emotional responses to specific stimuli [7, 10, 11, 12]. According to neuroscientific research, ERP components N200, P300, andlate positive potential (LPP) are closely associated with emotional valence to stimuli (e.g., preference, positivity, or negativity). When users like or dislike a given spatial stimulus, measurable differences in these ERP components can be observed. Further, recent artificial intelligence research shows that convolutional neural network (CNN) long short-term memory (LSTM) models, which capture both the spatial and the temporal properties of EEG data, can effectively learn subtle neural patterns to improve the accuracy of emotion classification [13]. Therefore, we hypothesize the following:  
1. EEG patterns, particularly ERP components (e.g., N200, P300, and LPP), will significantly differ between preferred and non-preferred stimuli.
2. A deep learning model trained on EEG signals can effectively detect these distinctions in ERP components, thereby achieving a high accuracy in classifying affective responses (i.e., preferred vs non-preferred) to architectural spaces.  
By quantitatively measuring users' affective experiences in architectural environments using EEG and ERP analysis and integrating these findings with deep learning techniques, we propose a methodology for personalized design. This approach is expected to overcome the limitations of traditional architectural design methods while contributing to a more scientific and objective process for affective architectural design.  
This paper briefly reviews the previous research on the relationships between architectural spaces, emotions, and preferences and the theoretical background of EEG-based emotion analysis. It then details the procedures used to preprocess and analyze the EEG data and design and evaluate the model. Through these steps, we aim to establish a process for quantitatively understanding users' emotional responses to architectural spaces and building a foundational dataset for personalized space design. Ultimately, we show the potential of neurophysiological approaches in architectural practice for incorporating user experiences more deeply, contributing to developments in affective architecture and real-time adaptive space design.