Enhancing lighting design through the investigation of illuminance and correlated color Temperature's effects on brain activity: An EEG-VR approach

Abstract

Investigating human responses to light can reveal important information with the potential to improve environmental design, circadian health, cognitive performance, and overall wellbeing. In this study, the researchers used VR immersion, EEG, and a machine-learning approach to better understand the relationship between brain activity and two important lighting properties—the illumination level and the correlated color temperature (CCT). Participants (N = 25) were asked to experience 17 different artificial lighting conditions and rate their perceived arousal and pleasure levels, and then adjust the lighting to their optimal preference. The results from the experiment demonstrated an association of illumination level and CCT with specific EEG band-features in the frontal and parietal brain regions. Our machine-learning classification approach was able to predict participants' behavioral choices of desired vs. non-desired lighting based on the EEG data from their first 10 s of exposure, a finding that has notable implications for the potential development of brain–computer interfaces for automatic lighting adjustment.