Identifying the Optimal Location of Facial EMG for Emotion Detection Using Logistic Regression.

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.