School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
Department of Sensor and Biomedical Technology, VIT University, Vellore campus, Vellore, India.
Stud Health Technol Inform. 2023 Jun 29;305:81-84. doi: 10.3233/SHTI230429.
In this study, we analyzed the utility of electromyogram (EMG) signals recorded from the zygomaticus major (zEMG), the trapezius (tEMG), and the corrugator supercilii (cEMG) for emotion detection. We computed eleven-time domain features from the EMG signals to classify the emotions such as amusing, boring, relaxing, and scary. The features were fed to the logistic regression, support vector machine, and multilayer perceptron classifiers, and model performance was evaluated. We achieved an average 10-fold cross-validation classification accuracy of 67.29%. 67.92% and 64.58% by LR using the features extracted from the EMG signals recorded from the zEMG, tEMG, and cEMG, respectively. The classification accuracy improved to 70.6% while combining features from the zEMG and cEMG for the LR model. However, the performance dropped while including the features of EMG from all three locations. Our study shows the importance of utilizing the zEMG and cEMG combination for emotion recognition.
在这项研究中，我们分析了从颧大肌（zEMG）、斜方肌（tEMG）和皱眉肌（cEMG）记录的肌电图（EMG）信号在情绪检测中的效用。我们从 EMG 信号中计算了十一个时域特征，以对有趣、无聊、放松和恐惧等情绪进行分类。这些特征被输入到逻辑回归、支持向量机和多层感知机分类器中，并评估了模型性能。我们通过使用从 zEMG、tEMG 和 cEMG 记录的 EMG 信号中提取的特征，分别实现了平均 10 倍交叉验证分类准确率为 67.29%、67.92%和 64.58%的 LR。当将 zEMG 和 cEMG 的特征结合到 LR 模型中时，分类准确率提高到了 70.6%。然而，当包括来自三个位置的所有 EMG 特征时，性能会下降。我们的研究表明，利用 zEMG 和 cEMG 的组合进行情绪识别非常重要。
