Ebato Yuki, Saki Tomita, Wakita Isshu, Ueno Ayumu, Ishibashi Tomoaki, Takahashi Tetsuya, Nobukawa Sou
Department of Computer Science, Chiba Institute of Technology, Narashino, Japan.
Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan.
Front Hum Neurosci. 2025 Jul 29;19:1605577. doi: 10.3389/fnhum.2025.1605577. eCollection 2025.
Fear is a fundamental emotion essential for survival; however, excessive fear can lead to anxiety disorders and other adverse consequences. Monitoring fear states is crucial for timely intervention and improved mental well-being. Although functional magnetic resonance imaging (fMRI) has provided valuable insights into the neural networks associated with fear, its high cost and environmental constraints limit its practical application in daily life. Electroencephalography (EEG) offers a more accessible alternative but struggles to capture deep brain activity. Physiological measures such as pupil dynamics and heart rate can provide indirect insights into these deeper processes, yet they are often studied in isolation. In this context, we aimed to evaluate the practical effectiveness and limitations of a multimodal approach that combines pupil dynamics and heart rate-indirect indicators of deep brain activity-with EEG, a temporally precise but spatially limited measure of cortical responses.
We simultaneously recorded EEG, pupillometry, and heart rate in 40 healthy male participants exposed to fear-inducing and neutral visual stimuli, while also assessing their psychological states.
Fear-inducing stimuli elicited distinct physiological responses, including increased occipital theta power, pupil dilation, and decreased heart rate. Notably, pupil size was the most sensitive discriminator of emotional state, though the integration of modalities yielded only limited improvement in classification accuracy.
These findings provide empirical support for the feasibility of multimodal physiological monitoring of fear and underscore the need for further refinement for real-world applications.
恐惧是生存所必需的一种基本情绪;然而,过度恐惧会导致焦虑症和其他不良后果。监测恐惧状态对于及时干预和改善心理健康至关重要。尽管功能磁共振成像(fMRI)为与恐惧相关的神经网络提供了有价值的见解,但其高成本和环境限制限制了其在日常生活中的实际应用。脑电图(EEG)提供了一种更易于使用的替代方法,但难以捕捉深部脑活动。诸如瞳孔动态和心率等生理指标可以为这些更深层次的过程提供间接见解,但它们通常是单独进行研究的。在此背景下,我们旨在评估一种多模态方法的实际有效性和局限性,该方法将瞳孔动态和心率(深部脑活动的间接指标)与EEG相结合,EEG是一种时间上精确但空间上有限的皮层反应测量方法。
我们在40名健康男性参与者暴露于诱发恐惧和中性视觉刺激时,同时记录了EEG、瞳孔测量和心率,同时还评估了他们的心理状态。
诱发恐惧的刺激引发了不同的生理反应,包括枕叶θ波功率增加、瞳孔扩张和心率降低。值得注意的是,瞳孔大小是情绪状态最敏感的判别指标,尽管多模态整合在分类准确性方面仅产生了有限的改善。
这些发现为恐惧的多模态生理监测的可行性提供了实证支持,并强调了在实际应用中进一步完善的必要性。