背景:虚拟现实晕动病(VRMS)是阻碍虚拟现实技术发展的关键问题,而准确检测其发生是解决问题的首要前提。
目的:在本文中,提出了一种基于多尺度特征相关性的卷积神经网络(CNN)脑电检测模型,用于检测VRMS。
方法:该模型使用多尺度一维卷积层从多导联EEG数据中提取多尺度时间特征,然后计算提取的多尺度特征在所有引线之间的特征相关性,形成特征相邻矩阵,它将时域特征转换为基于相关性的大脑网络特征,从而加强特征表示。最后,各层的相关特征融合。融合的特征然后被馈送到信道关注模块以过滤信道并使用完全连接的网络对其进行分类。最后,我们招募受试者体验6种不同模式的虚拟过山车场景,并收集任务前后的静息脑电数据对模型进行验证。
结果:结果表明,精度,该模型对VRMS识别的召回率和F1分数为98.66%,98.65%,98.68%,98.66%,分别。所提出的模型优于当前经典和先进的EEG识别模型。
结论:表明该模型可用于基于静息状态EEG的VRMS识别。
BACKGROUND: Virtual reality motion sickness (VRMS) is a key issue hindering the development of virtual reality technology, and accurate detection of its occurrence is the first prerequisite for solving the issue.
OBJECTIVE: In this paper, a convolutional neural network (CNN) EEG detection model based on multi-scale feature correlation is proposed for detecting VRMS.
METHODS: The model uses multi-scale 1D convolutional layers to extract multi-scale temporal features from the multi-lead EEG data, and then calculates the feature correlations of the extracted multi-scale features among all the leads to form the feature adjacent matrixes, which converts the time-domain features to correlation-based brain network features, thus strengthen the feature representation. Finally, the correlation features of each layer are fused. The fused features are then fed into the channel attention module to filter the channels and classify them using a fully connected network. Finally, we recruit subjects to experience 6 different modes of virtual roller coaster scenes, and collect resting EEG data before and after the task to verify the model.
RESULTS: The results show that the accuracy, precision, recall and F1-score of this model for the recognition of VRMS are 98.66 %, 98.65 %, 98.68 %, and 98.66 %, respectively. The proposed model outperforms the current classic and advanced EEG recognition models.
CONCLUSIONS: It shows that this model can be used for the recognition of VRMS based on the resting state EEG.