arXiv 2012.008552020-12-01cs.HC

虚拟现实中基于EEG的Cybersickness分类的深度学习方法综述

A Review of Deep Learning Approaches to EEG-Based Classification of Cybersickness in Virtual Reality

Caglar Yildirim

备注：: 7 pages

摘要：

赛博病是暴露于虚拟现实（VR）体验的令人不愉快的副作用，并且是指响应于VR暴露而触发的恶心和头晕等生理反应。鉴于虚拟现实中晕机对用户体验的削弱作用，近年来学术界对从生理测量中自动检测晕机的兴趣达到了顶峰。脑电图（EEG）已被广泛用于捕获大脑中的电活动的变化，并使用各种机器学习算法从脑波中自动分类电脑病。深度学习（DL）算法的最新进展和DL的计算资源的增加的可用性已经为将DL框架应用于基于EEG的网络病检测的新研究领域铺平了道路。因此，本次审查涉及的同行评议的论文有关的DL框架的应用，从EEG信号的cybersickness的分类系统的审查。通过详尽的数据库检索确定了相关文献，并根据数据汇集、数据预处理和DL架构的实验方案对论文进行了仔细审查。该综述揭示了这一新兴研究领域的研究数量有限，并表明这些研究中报告的DL框架（即，DNN、CNN和RNN）可以对晕网病进行分类，平均准确率为93%。本文综述了DL框架在基于EEG的晕电检测中的应用趋势和问题，并为未来的研究提供了一些指导。

英文摘要：

Cybersickness is an unpleasant side effect of exposure to a virtual reality (VR) experience and refers to such physiological repercussions as nausea and dizziness triggered in response to VR exposure. Given the debilitating effect of cybersickness on the user experience in VR, academic interest in the automatic detection of cybersickness from physiological measurements has crested in recent years. Electroencephalography (EEG) has been extensively used to capture changes in electrical activity in the brain and to automatically classify cybersickness from brainwaves using a variety of machine learning algorithms. Recent advances in deep learning (DL) algorithms and increasing availability of computational resources for DL have paved the way for a new area of research into the application of DL frameworks to EEG-based detection of cybersickness. Accordingly, this review involved a systematic review of the peer-reviewed papers concerned with the application of DL frameworks to the classification of cybersickness from EEG signals. The relevant literature was identified through exhaustive database searches, and the papers were scrutinized with respect to experimental protocols for data collection, data preprocessing, and DL architectures. The review revealed a limited number of studies in this nascent area of research and showed that the DL frameworks reported in these studies (i.e., DNN, CNN, and RNN) could classify cybersickness with an average accuracy rate of 93%. This review provides a summary of the trends and issues in the application of DL frameworks to the EEG-based detection of cybersickness, with some guidelines for future research.

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