Evidence for human-centric in-vehicle lighting: part 3—Illumination preferences based on subjective ratings, eye-tracking behavior, and EEG features - Weirich, Christopher; Lin, Yandan; Khanh, Tran Quoc (Frontiers in Human Neuroscience, 2023)
Within this third part of our miniseries, searching for the best and worst automotive in-vehicle lighting settings, we aim to extend our previous finding about white light illumination preferences by adding local cortical area activity as one key indicator. Frontal electrical potential asymmetry, measured by electroencephalogram (EEG), is a highly correlated index to identify positive and negative emotional behavior, primary in the alpha band. It is rarely understood to what extent this observation can be applied to evaluate subjective preference or dislike based on luminaire variations in hue, chroma and lightness. Within a controlled laboratory study, we investigated eight study participants answering this question by showing highly immersive 360° image renderings. Doing so, we first subjectively defined, based on four different external driving scenes varying in location and time settings, the best and worst luminaire settings by changing six unlabeled luminaire sliders. Emotional feedback was collected based on semantic differentials and an emotion wheel. Further, we recorded 120 Hz gaze data to identify the most important in-vehicle area of interest during the luminaire adaptation process. In the second study session, we recorded EEG data during a monocular observation task of repeated images arbitrarily paired by previously defined best and worst lighting settings and separated between all four driving scenes. Results from gaze data showed that the central vehicle windows with the left-side orientated colorful in-vehicle fruit table were both significantly longer fixed compared to other image areas. Furthermore, the previously identified cortical EEG feature describing the maximum power spectral density could successfully separate positive and negative luminaire settings based only on cortical activity. Within the four driving scenes, two external monotonous scenes followed trendlines defined by highly emotional correlated images. More interesting external scenes contradicted this trend, suggesting an external emotional bias stronger than the emotional changes created by luminaires. Therefore, we successfully extended our model to define the best and worst in-vehicle lighting with cortical features by touching the field of neuroaesthetics.