Towards emotionally intelligent buildings: A Convolutional neural network based approach to classify human emotional experience in virtual built environments

Accurate classification of human emotions in designed spaces is essential for architects and engineers, who aim to maximize positive emotions by configuring architectural design features. Previous studies at the conjunction of neuroscience and architecture confirmed the impact of architectural design features on human emotions. Recent development of biometric sensors enabled researchers to identify emotions by measuring human physiological responses (e.g., the use of electroencephalogram (EEG) to measure brain activities). However, a gap in the knowledge exists in terms of an accurate classification model for human emotions in design variants. This study proposed a convolutional neural network (CNN) based approach to classify human emotions. The approach considered two types of CNN architectures as CNN ensemble and auto-encoders. The inputs of these CNN algorithms were 2D images generated by projecting the frequency band power of EEG onto the scalp graph in accordance with the electrode placements. This transformation from time-series EEG data to 2D frequency band power images retain the spatial, time and frequency domain features from participants’ brain dynamics. Performance of the proposed approach was validated using multiple metrics, including precision, recall, f-1 score, and Area Under Curve (AUC). Results showed that the auto-encoder based approach achieved the best performance with an AUC of 0.95.