Vection, the visual illusion of self-motion, provides a strong marker of the VR user experience and plays an important role in both presence and cybersickness. Traditional measurements have been conducted using questionnaires, which exhibit inherent limitations due to their subjective nature and preventing real-time adjustments. Detecting vection in real time would allow VR systems to adapt to users' needs, improving comfort and minimizing negative effects like motion sickness. This paper investigates the presence of vection markers in electroencephalogram (EEG) brain signals using evoked potentials (brain responses to external stimulations).
We designed a VR experiment that induces vection using two conditions: (1) forward acceleration or (2) backward acceleration. We recorded both electroencephalographic (EEG) signals and gathered subjective reports on thirty (30) participants. We found an evoked potential of vection characterized by a positive peak around 600 ms (P600) after stimulus onset in the parietal region and a simultaneous negative peak in the frontal region. Our results also found participant variability in sensitivity to vection and cybersickness and EEG markers of acceleration across subjects. This result is promising for potential detection of vection using EEG and paves the way for future studies towards a better understanding of vection. It also provides insights into the functional role of the visual system and its integration with the vestibular system during motion-perception. It has the potential to help enhance VR user experience by qualifying users' perceived vection and adapting the VR environments accordingly.
Dataset of an EEG-based BCI experiment in Virtual Reality and on a Personal Computer
我们描述了一个数据集的实验过程，我们已经在https://doi.org/10.5281/zenodo.2605204上以mat（Mathworks，Natick，USA）和CSV格式公开提供。该数据集包含21名受试者在PC（个人计算机）和VR（虚拟现实）上进行视觉P300实验的脑电图记录。视觉P300是由视觉刺激引起的事件相关电位，在刺激开始后240-600 ms达到峰值。该实验的目的是为了比较使用基于P300的脑机接口在PC上，并与虚拟现实耳机，有关的生理，主观和性能方面。脑机接口是基于脑电图（EEG）的。脑电图记录感谢16个电极。该虚拟现实头戴式设备由无源盔装显示器组成，即，除了智能手机之外不包括任何电子设备的盔装显示器。该实验于2018年在GIPSA实验室（格勒诺布尔阿尔卑斯大学，CNRS，Grenoble-INP）进行，并由IHMTEK公司（Interaction Homme-Machine Technologie）推广。该研究得到了格勒诺布尔-阿尔卑斯大学伦理委员会（Comit{é} d 'Escherque pour la Recherche Non-Interventionnelle）的批准。用于操作数据的Python代码可在https://github.com/plcrodrigues/py.VR.EEG.2018-GIPSA上获得。该数据集的ID为VR.EEG.…
Anticipation Before Action: EEG-Based Implicit Intent Detection for Adaptive Gaze Interaction in Mixed Reality
LMU Munich（慕尼黑大学）；Carl von Ossietzky Universität Oldenburg（奥尔登堡卡尔·冯·奥西茨基大学）；TU Dortmund University（多特蒙德工业大学）；Research Center Trustworthy Data Science and Security（可信数据科学与安全研究中心）
Monitoring mental workload by EEG during a game in Virtual Reality
在活动期间，了解用户的心理工作负荷（MWL）允许改善人机交互（HMI）。事实上，MWL对个人及其与环境的互动产生影响。因此，监测是一个至关重要的问题。在这种情况下，我们创建了基于N-back任务（通常用于测量MWL）的虚拟游戏Back to Pizza。在这个更有趣的变体中，用户必须执行比萨食品卡车的客户的订单。这是一个互动游戏，涉及IHM'23会议的观众，选择几个参数，如成分的数量。在此体验期间，目标是通过脑电图（EEG）实时的测量MWL，并向观众提供MWL水平的视觉反馈。通过这个演示，我们提出了一个虚拟互动游戏的概念，测量MWL在实时的。
Moving Beyond the Flat Screen: Opportunities and Challenges of Extended Reality for Studying Human Brain and Behavior
University of California, Santa Barbara（加州大学圣塔芭芭拉分校）；Institute for Collaborative Biotechnologies, University of California, Santa Barbara（加州大学圣塔芭芭拉分校联合生物技术创新研究所）；University of Huddersfield（哈德斯菲尔德大学）；Saarland University（萨尔兰大学）；University of Nebraska–Lincoln（内布拉斯加大学林肯分校）
Deep Learning-Based Visual Fatigue Detection Using Eye Gaze Patterns in VR
Dept. of Computer Science Clarkson University Potsdam, New York, USA（计算机科学系 克拉克逊大学）；David D. Reh School of Business Clarkson University Potsdam, New York, USA（戴维·D·雷商学院 克拉克逊大学）
