PDF(Pubmed)
Abstract：
To effectively detect motion sickness induced by virtual reality environments, we developed a classification model specifically designed for visually induced motion sickness, employing a phase-locked value (PLV) functional connectivity matrix and a CNN-LSTM architecture. This model addresses the shortcomings of traditional machine learning algorithms, particularly their limited capability in handling nonlinear data. We constructed PLV-based functional connectivity matrices and network topology maps across six different frequency bands using EEG data from 25 participants. Our analysis indicated that visually induced motion sickness significantly alters the synchronization patterns in the EEG, especially affecting the frontal and temporal lobes. The functional connectivity matrix served as the input for our CNN-LSTM model, which was used to classify states of visually induced motion sickness. The model demonstrated superior performance over other methods, achieving the highest classification accuracy in the gamma frequency band. Specifically, it reached a maximum average accuracy of 99.56% in binary classification and 86.94% in ternary classification. These results underscore the model\'s enhanced classification effectiveness and stability, making it a valuable tool for aiding in the diagnosis of motion sickness.
摘要：
为了有效检测由虚拟现实环境引起的晕动病，我们开发了一个专门为视觉诱发的晕动病设计的分类模型，采用锁相值(PLV)功能连接矩阵和CNN-LSTM架构。该模型解决了传统机器学习算法的不足,特别是他们处理非线性数据的能力有限。我们使用来自25位参与者的EEG数据构建了基于PLV的功能连接矩阵和六个不同频段的网络拓扑图。我们的分析表明，视觉诱发的晕动病显着改变了EEG中的同步模式，尤其影响额叶和颞叶。功能连接矩阵作为我们的CNN-LSTM模型的输入，用于对视觉诱发的晕动病的状态进行分类。该模型表现出优于其他方法的性能，在伽马频带中实现最高的分类精度。具体来说,它在二元分类中达到了99.56％的最大平均准确率，在三元分类中达到了86.94％。这些结果强调了模型增强的分类有效性和稳定性，使其成为帮助诊断晕车的有价值的工具。
