To establish an objective method of evaluating emotion which is applicable to the development of in-vehicle products, this study aims to detect driver’s “feeling of excitement” and “feeling of nervousness” by observed physiological indices. Assuming that “feeling of excitement” is to be considered a pleasant arousal emotion and “feeling of nervousness” is regarded as unpleasant arousal emotion, we carried out the experiment to verify whether the above two emotions can be distinguished by means of cerebral blood flow (CBF) and heart rate variability (HRV). Eight participants riding on a simulated self-driving car were presented with the visual stimuli with intention to evoke the above two emotions. The result showed that oxyhemoglobin increased in prefrontal cortex when “feeling of excitement” was evoked and decreased when “feeling of nervousness” was evoked, meanwhile SDNN/RMSSD increased in both situations. We clarified the above two emotions were distinguishable using measurement of CBF and HRV.

This research proposed a model of cerebral blood flow responses due to work in a work environment. Based on the model, features were proposed to capture the temporal changes and localization of cerebral blood flows, focusing on the periodicity of the task. A method to remove the effect of environmental noise in the features was also proposed. To verify the usefulness of the proposed method, we conducted an intellectual task discrimination test in an office environment under a color environment. In the verification experiment, we measured the cerebral blood flow of participants using NIRS when they performed three types of intellectual tasks in four different color environments. We used SVM to discriminate the data of cerebral blood flow using the features calculated for each arbitrary segment. The maximum correct response rate was 0.717 after comparing the segmented interval, type of hemoglobin, and type of kernel function.