Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach

Architectural elements—such as shapes, colors, and lighting—significantly influence how users emotionally respond to spaces. This study addresses the challenge of capturing unconscious and rapid emotional responses by employing a 32-channel electroencephalography (EEG) approach with 40 participants, who viewed multiple images of architectural spaces while real-time brain activity was recorded. Event-related potential (ERP) analysis focusing on N100, N200, P300, and late positive potential confirmed reliable differences in neural signals between preferred and non-preferred stimuli. Two convolutional neural network long short-term memory deep learning models were trained on the EEG data: one using all the ERP segments, and the other focusing on statistically significant ERP features. The first model achieved a high recall but a relatively lower precision, while the second improved accuracy and precision at the expense of recall. These findings suggest real-time, objective measures of users’ emotional responses can inform early-stage architectural design and reduce reliance on subjective evaluations. By integrating EEG-based insights into smart architecture or virtual reality simulations, designers may optimize building features to align with user preferences and well-being, contributing to the development of effective and user-centric built environments.