# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 5 Discussion  
This study confirms that users' emotional responses toward and preferences for architectural stimuli can be quantitatively evaluated using EEG. We proposed a methodology based on deep learning models to objectively classify these responses. We recruited a total of 40 participants and collected their neural responses to architectural images using a 32-channel EEG device. Thorough data preprocessing (filtering, noise removal, channel selection, and re-referencing) ensured high-quality EEG data. Subsequently, ERP components (N100, N200, P300, and LPP) were analyzed to identify spatiotemporal differences in brain responses between preferred and non-preferred stimuli. The results revealed significant differences in the P300 and LPP intervals, demonstrating that an ERP-based evaluation of architectural preferences is valid, supporting Hypothesis 1. The two CNN-LSTM deep learning models proposed in this research performed differently depending on the ERP features used. Model 1 (CNN-LSTM using the entire ERP) achieved a high recall of about 98.76%, minimizing missed detections of emotional responses, but its precision was relatively lower (72.11%). Model 2 (CNN-LSTM using \(t\)-test-selected ERP features) had an improved accuracy (81.90%) and precision (85.00%) but exhibited a lower recall (70.00%). This indicates that the entire ERP dataset can provide a broader range of emotional information but may be sensitive to noise, whereas selectively using statistically significant ERP features enhances precision but may overlook subtle neurological changes. Hence, ERP feature selection plays a crucial role in determining model performance, partially supporting Hypothesis 2. To further validate model performance and generalizability, a fivefold cross-validation was conducted. Although both models showed some variability across folds, Model 2 consistently demonstrated a superior and more stable performance. Moreover, a paired \(t\)-test revealed a statistically significant difference in accuracy between Model 1 and Model 2 (\(t=-10.4913\), \(p=0.0005\)), confirming that the ERP-based feature selection may be more effective for EEG-based emotion classification and model performance can vary according to how input data are constructed. Several important limitations should be noted. Firstly, the sample size (\(t=40\)) is relatively small, warranting caution in generalizing the results. Larger-scale follow-up studies are needed to improve reliability. Additionally, because EEG data may not fully conform to a normal distribution, we adopted a \(t\)-test for exploratory analysis. Future research should consider nonparametric approaches (e.g., Mann-Whitney U test) or multiple comparison corrections (e.g., false discovery rate) to enhance the robustness of statistical analyses. Finally, this study employed image-based stimuli. Therefore, long-term measures in real-world or VR spaces and the integration of other physiological signals (e.g., heart rate and galvanic skin response) are warranted to ensure ecological validity. Nevertheless, the value of the methodology proposed here is that it offers an objective approach to incorporating users' affective responses during the early stages of architectural design. Accordingly, the findings of this study should be considered in relation to recent EEG-based research in the field of neuroarchitecture. While this study highlights the feasibility of applying EEG-based emotion analysis in architectural design, several challenges must be addressed before it can be practically applied, as follows:  
* Real-world data collection: to enhance ecological validity, future researchers should conduct longer term measurements in real-life environments such as hospitals, parks, commercial complexes, and transportation facilities, where diverse contextual factors come into play [1,5].