Dual-pathway EEG model with channel attention for virtual reality motion sickness detection

IF 2.7 4区 医学 Q2 BIOCHEMICAL RESEARCH METHODS
Chengcheng Hua , Yuechi Chen , Jianlong Tao , Zhian Dai , Wenqing Yang , Dapeng Chen , Jia Liu , Rongrong Fu
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引用次数: 0

Abstract

Background

Motion sickness has been a key factor affecting user experience in Virtual Reality (VR) and limiting the development of the VR industry. Accurate detection of Virtual Reality Motion Sickness (VRMS) is a prerequisite for solving the problem.

New method

In this paper, a dual-pathway model with channel attention for detecting VRMS is proposed. The proposed model has two pathways that both consist of CNN blocks and channel attention modules. The first pathway takes the EEG signal as inputs. The second pathway transforms the EEG signal into brain networks of six frequency bands using Phase Locking Value (PLV) or ρ index (RHO) methods and takes the adjacent matrixes as input. The features from the two pathways are connected and fed into the fully connected layer for classification. Finally, a VR flight simulation experiment is performed and the EEG of the resting state before and after the virtual flight task are collected to validate the model.

Results

The average accuracy, precision, recall, and F1 score of the proposed model are 99.12 %, 99.12 %, 99.11 %, and 99.12 %, respectively.

Comparison with existing methods

Eight models are introduced as the reference methods and four of them are fused as the hybrid models in this study. The results show that the proposed model is better than those state-of-art models.

Conclusions

The proposed model outperforms the state-of-the-art models and provides objective and direct guidance for overcoming VRMS and optimizing VR experience.
基于通道关注的虚拟现实晕动病检测双通路脑电模型。
背景:晕动病已经成为影响虚拟现实(VR)用户体验的关键因素,限制了VR产业的发展。虚拟现实晕动病的准确检测是解决这一问题的前提。新方法:提出了一种具有通道关注的VRMS检测双路径模型。该模型有两个路径,分别由CNN块和频道关注模块组成。第一种路径以脑电信号为输入。第二种途径采用锁相值(PLV)或ρ指数(RHO)方法将脑电信号转换成6个频带的脑网络,并将相邻矩阵作为输入。两个路径的特征被连接并输入到全连接层中进行分类。最后进行虚拟现实飞行仿真实验,采集虚拟飞行任务前后静息状态的脑电,对模型进行验证。结果:模型的平均准确率为99.12%,精密度为99.12%,召回率为99.11%,F1分数为99.12%。与现有方法的比较:本研究引入8种模型作为参考方法,并将其中4种模型融合为混合模型。结果表明,该模型优于现有的模型。结论:该模型优于现有模型,为克服VRMS、优化VR体验提供了客观、直接的指导。
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来源期刊
Journal of Neuroscience Methods
Journal of Neuroscience Methods 医学-神经科学
CiteScore
7.10
自引率
3.30%
发文量
226
审稿时长
52 days
期刊介绍: The Journal of Neuroscience Methods publishes papers that describe new methods that are specifically for neuroscience research conducted in invertebrates, vertebrates or in man. Major methodological improvements or important refinements of established neuroscience methods are also considered for publication. The Journal''s Scope includes all aspects of contemporary neuroscience research, including anatomical, behavioural, biochemical, cellular, computational, molecular, invasive and non-invasive imaging, optogenetic, and physiological research investigations.
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