Modulation of Driver's Emotional States by Manipulating In-Vehicle Environment: Validation With Biosignals Recorded in An Actual Car Environment

Abstract

A driver's emotional state can affect driving performance. According to the studies on the driving performance based on the circumplex (arousal-valence) model of affect, negative emotions such as anger and sadness can severely hinder safe driving. In this study, we developed a system to modulate drivers' emotions to designated emotional states by manipulating in-vehicle environments, such as ambient lighting, background music, scent, ventilation, and rear curtains. The proposed system, named the “mood-modulator” system, consists of four different modes, designed to induce different emotional states. The feasibility of the “mood-modulator” system was evaluated using electroencephalogram (EEG) and photoplethysmogram (PPG) signals recorded from 48 drivers in an actual car environment. In the experiments, negative emotions were induced for each participant using short movie clips. Then, one of the four modes (different in-vehicle environments) was executed, during which both EEG and PPG data were acquired. We quantitatively evaluated whether each mode could effectively induce targeted emotional valence using machine learning classifier models, individually constructed from EEG data recorded during calibration sessions. The modulation of emotional arousal by each mode was also assessed using heart rate and respiration rate extracted from the PPG data. Our results demonstrated that the four modes could effectively increase the participant's emotional valence and modulate emotional arousal state to the intended direction. To the best of our knowledge, this is the first study to quantitatively evaluate a system that modulates a driver's emotional state using biosignals recorded in an actual car.