# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 6 Conclusions  
Based on EEG and ERP analysis, the findings of this research demonstrate that users' emotional preferences for architectural stimuli can be quantified. Notably, distinct variations between preferred and non-preferred stimuli were identified in the P300 and LPP components, suggesting that EEG-based emotion classification is neurophysiological valid. When evaluating the CNN-LSTM deep learning models, Model 1, which utilized the entire ERP dataset, achieved a high recall but a relatively lower precision. In contrast, Model 2, which selectively incorporated only statistically significant ERP features, demonstrated a high accuracy and precision but a lower recall. Results from the fivefold cross-validation further confirmed the more consistent and superior performance of Model 2. A paired _t_-test indicated a statistically significant difference in accuracy between the two models, supporting the effectiveness of selective ERP feature extraction. These findings underscore the potential of EEG-based emotion classification as an objective and neurophysiological valid evaluation tool while emphasizing the importance of identifying relevant ERP components.  
Nonetheless, the study is limited by a small sample size, assumptions of data normality, and concerns regarding ecological validity. Future research should address these limitations by expanding the participant pool, gathering data in real-world settings, and integrating multiple physiological signals.  
Moreover, this work highlights a promising approach to embedding user emotions and preferences into the architectural design process. As real-world validation expands, user diversity increases, and technical methodologies become more refined, these neurophysiological strategies may significantly contribute to the development of truly human-centric, affective architectural environments.  
Conceptualization, J.E.C. and H.J.J.; Methodology, J.E.C. and S.Y.K.; Software, J.E.C., S.Y.K. and Y.Y.H.; Validation, J.E.C.; Formal analysis, J.E.C. and Y.Y.H.; Data curation, J.E.C., S.Y.K. and Y.Y.H.; Writing--original draft, J.E.C.; Writing--review & editing, J.E.C., S.Y.K. and Y.Y.H.;Supervision, J.E.C. and S.Y.K.; Project administration, H.J.J.; Funding acquisition, H.J.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2022R1A2C3011796).  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Hanyang University (HYUIRB-202504-006, approved on 22 March 2025).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the corresponding author.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  
The following abbreviations are used in this manuscript:  
Adam Adaptive moments of gradient CNN Convolutional neural networks CNN-LSTM Convolutional neural network long short-term memory EEG Electroencephalography ERP Event-related potential LPP Late positive potential LSTM Long short-term memory VR Virtual reality