VR sickness measurement with EEG using DNN algorithm

D. Jeong, Sangbong Yoo, Yun Jang
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引用次数: 11

Abstract

Recently, VR technology is rapidly developing and attracting public attention. However, VR Sickness is a problem that is still not solved in the VR experience. The VR sickness is presumed to be caused by crosstalk between sensory and cognitive systems [1]. However, since there is no objective way to measure sensory and cognitive systems, it is difficult to measure VR sickness. In this paper, we collect EEG data while participants experience VR videos. We propose a Deep Neural Network (DNN) deep learning algorithm by measuring VR sickness through electroencephalogram (EEG) data. Experiments have been conducted to search for an appropriate EEG data preprocessing method and DNN structure suitable for the deep learning, and the accuracy of 99.12% is obtained in our study.
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基于DNN算法的脑电VR疾病测量
近年来,虚拟现实技术正在迅速发展并引起了公众的关注。然而,在VR体验中,VR病仍然是一个没有解决的问题。VR病被认为是由感觉和认知系统之间的串扰引起的[1]。然而,由于没有客观的方法来测量感觉和认知系统,因此很难测量VR疾病。在本文中,我们收集了参与者在观看VR视频时的脑电图数据。我们提出了一种深度神经网络(DNN)深度学习算法,通过脑电图(EEG)数据来测量VR疾病。通过实验寻找合适的脑电数据预处理方法和适合深度学习的DNN结构,获得了99.12%的准确率。
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