Cybersickness remains a major issue that can severely impact the user’s comfort, performance, and enjoyment of VR. While there are various approaches to combat cybersickness, only a few have been developed for real-time mitigation based on user biofeedback, and these do not aim to distinguish causal factors and apply mitigation accordingly. In this paper, we propose a novel real-time cybersickness detection and mitigation system (CDMS) that leverages a two-stage shallow convolutional network to detect cybersickness and identify the contributing factors from the user’s electroencephalogram (EEG) activity. Based on the output of the convolutional network, CDMS adaptively modifies the parameters of the identified factor in the generated virtual environment to mitigate the onset cybersickness. For this, we conjointly consider three major content factors of cybersickness: navigation speed, scene complexity, and stereoscopic rendering. To train the network, we collected EEG data and self-reports of cybersickness from the subjects by simulating these factors in varying degrees of severity. For the performance evaluation of CDMS, we conducted a user study comprising one CDMS session and two different control sessions. The results show that the users experienced significantly less cybersickness after the CDMS session. Also, CDMS effectively avoided false positives that could otherwise degrade the VR experience.

中文翻译：

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CDMS：通过自适应调整 VR 内容因素来缓解脑电图引导晕眩症的实时系统

晕眩症仍然是一个严重影响用户舒适度、性能和 VR 享受的主要问题。虽然对抗晕眩症的方法有很多种，但只有少数几种方法能够根据用户生物反馈进行实时缓解，而且这些方法的目的并不是区分因果因素并相应地应用缓解措施。在本文中，我们提出了一种新颖的实时晕眩检测和缓解系统（CDMS），该系统利用两级浅卷积网络来检测晕眩并从用户的脑电图（EEG）活动中识别影响因素。基于卷积网络的输出，CDMS自适应地修改生成的虚拟环境中已识别因素的参数，以减轻晕眩症的发作。为此，我们综合考虑晕眩症的三大内容因素：导航速度、场景复杂度、立体渲染。为了训练网络，我们通过模拟不同严重程度的这些因素，收集了受试者的脑电图数据和晕机症状的自我报告。为了评估 CDMS 的性能，我们进行了一项用户研究，包括一个 CDMS 会话和两个不同的控制会话。结果表明，在 CDMS 会话结束后，用户的晕机症状明显减轻。此外，CDMS 还有效避免了误报，否则可能会降低 VR 体验。