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.

中文翻译：

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通过操纵车内环境调节驾驶员的情绪状态：使用实际汽车环境中记录的生物信号进行验证


驾驶员的情绪状态会影响驾驶表现。根据基于情感环波（唤醒效价）模型的驾驶表现研究，愤怒、悲伤等负面情绪会严重阻碍安全驾驶。在这项研究中，我们开发了一种系统，通过操纵车内环境（例如环境照明、背景音乐、气味、通风和后窗帘）将驾驶员的情绪调节到指定的情绪状态。该系统被称为“情绪调节器”系统，由四种不同的模式组成，旨在诱发不同的情绪状态。使用 48 名驾驶员在实际汽车环境中记录的脑电图 (EEG) 和光电体积描记图 (PPG) 信号评估了“情绪调节器”系统的可行性。在实验中，使用短片诱发每个参与者的负面情绪。然后，执行四种模式（不同的车内环境）之一，在此期间采集 EEG 和 PPG 数据。我们使用机器学习分类器模型定量评估每种模式是否可以有效诱导目标情绪效价，这些模型是根据校准过程中记录的脑电图数据单独构建的。还使用从 PPG 数据中提取的心率和呼吸率来评估每种模式对情绪唤醒的调节。我们的结果表明，四种模式可以有效提高参与者的情绪效价并将情绪唤醒状态调节至预期方向。据我们所知，这是第一项利用实际汽车中记录的生物信号来定量评估调节驾驶员情绪状态的系统的研究。