Neural Correlates of Architectural Interior Preferences and Single-Trial Preference Prediction

Abstract

Preference is an important indicator of architectural design quality and human well-being. Current interior space design mainly relies on the designer's subjective judgment and lacks an objective basis. This study aims to quantify event-related potential (ERP) features of architectural interior preference, and examine whether we can infer human preference from single-trial ERP using machine learning. Thirty-six university students participated in an experiment where they viewed architectural interior images and rated them based on their preferences. Significant voltage differences were observed in particular channels (mainly in Oz, O2, Pz, Fp1, Fp2, T7) when participants viewed liked versus disliked images. Source localization indicated that liked images primarily activated the left frontal cortex, while disliked images predominantly activated the left occipital lobe. The within-subject models significantly outperformed the chance level, while the cross-subject models did not show significant results. Also, we found that some visual features can be decoded better than other features by EEG. These findings shed new light on understanding the difference in ERP of interior preference and illustrate the potential for developing a brain-computer interface (BCI) for rapid design evaluation.

Keywords: Architectural preference, EEG (Electroencephalography), ERP (Event-related potential), Preference prediction, Machine learning

DOI: 10.54941/ahfe1006610