This study examined emotional responses to AI-generated biodigital architecture images using electroencephalographic (EEG) data. In a pre-experiment, 336 participants identified 60 images from an initial pool of 600 that elicited strong emotional responses classified as awe, disgust, or content. EEG recordings were then conducted with 52 volunteers, with channel selection and sample size estimation based on existing dataset analysis. The results showed that the gamma and delta bands yielded the highest classification accuracy, with the gamma band achieving an accuracy of 77.07% ± 13.8% for awe emotion. Key factors such as greenery and non-uniform granularity were linked to positive emotions, while dampness triggered negative reactions. These results emphasize the importance of incorporating natural elements and varied textures in biodigital architecture to enhance aesthetic appeal and acceptance. The study demonstrates EEG's capability to objectively assess architectural preferences, providing valuable insights for architects to design engaging and sustainable environments.
Blogger's Review: This research highlights a profound connection between biodigital architecture design and human emotional responses through EEG technology. The integration of natural elements not only enhances aesthetics but also significantly influences user psychological experiences. Future architectural designs can leverage these insights to create more appealing spaces.