Virtual Reality (VR) allows users to interact with 3D immersive environments and has the potential to be a key technology across many domain applications, including access to a future metaverse. Yet, consumer adoption of VR technology is limited by cybersickness (CS)—a debilitating sensation accompanied by a cluster of symptoms, including nausea, oculomotor issues and dizziness. A leading problem is the lack of automated objective tools to predict or detect CS in individuals, which can then be used for resistance training, timely warning systems or clinical intervention. This paper explores the spatiotemporal brain dynamics and heart rate variability involved in cybersickness and uses this information to both predict and detect CS episodes. The present study applies deep learning of EEG in a spiking neural network (SNN) architecture to predict CS prior to using VR (85.9%, F7) and detect it (76.6%, FP1, Cz). ECG-derived sympathetic heart rate variability (HRV) parameters can be used for both prediction (74.2%) and detection (72.6%) but at a lower accuracy than EEG. Multimodal data fusion of EEG and sympathetic HRV does not change this accuracy compared to ECG alone. The study found that Cz (premotor and supplementary motor cortex) and O2 (primary visual cortex) are key hubs in functionally connected networks associated with both CS events and susceptibility to CS. F7 is also suggested as a key area involved in integrating information and implementing responses to incongruent environments that induce cybersickness. Consequently, Cz, O2 and F7 are presented here as promising targets for intervention.

中文翻译：

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虚拟现实引起的晕眩症的预测和检测：使用时空脑电图大脑数据和心率变异性的尖峰神经网络方法

虚拟现实 (VR) 允许用户与 3D 沉浸式环境进行交互，并有可能成为跨许多领域应用程序的关键技术，包括访问未来的元宇宙。然而，消费者对 VR 技术的采用受到晕眩症 (CS) 的限制，晕眩症是一种令人衰弱的感觉，伴有一系列症状，包括恶心、动眼神经问题和头晕。主要问题是缺乏自动化客观工具来预测或检测个体的 CS，然后可用于阻力训练、及时预警系统或临床干预。本文探讨了晕机症所涉及的时空大脑动力学和心率变异性，并利用这些信息来预测和检测晕机发作。本研究在尖峰神经网络 (SNN) 架构中应用脑电图深度学习，在使用 VR 之前预测 CS（85.9%，F7）并检测它（76.6%，FP1，Cz）。ECG 衍生的交感心率变异性 (HRV) 参数可用于预测 (74.2%) 和检测 (72.6%)，但精度低于 EEG。与单独的心电图相比，脑电图和交感心率变异的多模态数据融合不会改变这种准确性。研究发现，Cz（运动前皮层和辅助运动皮层）和 O2（初级视觉皮层）是与 CS 事件和 CS 易感性相关的功能连接网络中的关键枢纽。F7 还被建议作为一个关键领域，涉及整合信息和对导致晕机症的不一致环境实施响应。因此，Cz，