Recent studies have indicated that facial electromyogram (fEMG)-based facial-expression recognition (FER) systems are promising alternatives to the conventional camera-based FER systems for virtual reality (VR) environments because they are economical, do not depend on the ambient lighting, and can be readily incorporated into existing VR headsets. In our previous study, we applied a Riemannian manifold-based feature extraction approach to fEMG signals recorded around the eyes and demonstrated that 11 facial expressions could be classified with a high accuracy of 85.01%, with only a single training session. However, the performance of the conventional fEMG-based FER system was not high enough to be applied in practical scenarios. In this study, we developed a new method for improving the FER performance by employing linear discriminant analysis (LDA) adaptation with labeled datasets of other users. Our results indicated that the mean classification accuracy could be increased to 89.40% by using the LDA adaptation method (p < .001, Wilcoxon signed-rank test). Additionally, we demonstrated the potential of a user-independent FER system that could classify 11 facial expressions with a classification accuracy of 82.02% without any training sessions. To the best of our knowledge, this was the first study in which the LDA adaptation approach was employed in a cross-subject manner. It is expected that the proposed LDA adaptation approach would be used as an important method to increase the usability of fEMG-based FER systems for social VR applications.

最近的研究表明，基于面部肌电图 (fEMG) 的面部表情识别 (FER) 系统是用于虚拟现实 (VR) 环境的传统基于相机的 FER 系统的有希望的替代品，因为它们经济实惠，不依赖于环境照明，并且可以很容易地集成到现有的 VR 耳机中。在我们之前的研究中，我们将基于黎曼流形的特征提取方法应用于眼睛周围记录的 fEMG 信号，并证明只需一次训练即可以 85.01% 的高精度对 11 种面部表情进行分类。然而，传统的基于 fEMG 的 FER 系统的性能不足以应用于实际场景。在这项研究中，我们开发了一种通过对其他用户的标记数据集进行线性判别分析 (LDA) 自适应来提高 FER 性能的新方法。我们的结果表明，使用 LDA 自适应方法可以将平均分类准确率提高到 89.40%（p  < .001，Wilcoxon 符号秩检验）。此外，我们展示了独立于用户的 FER 系统的潜力，该系统可以对 11 种面部表情进行分类，分类准确率为 82.02%，无需任何培训课程。据我们所知，这是第一项以跨学科方式采用 LDA 适应方法的研究。预计提出的 LDA 适应方法将用作增加基于 fEMG 的 FER 系统在社交 VR 应用中的可用性的重要方法。