A proposed deep learning model for EEG-based emotion recognition achieved an accuracy of 70.89% in binary-class classification and 90.33% in multi-class classification.
A proposed deep learning model using VMD and EMD transformed EEG signals achieved 90.33% accuracy in multi-class emotion recognition.
Emotion can be defined as a voluntary or involuntary reaction to external factors. People express their emotions through actions, such as words, sounds, facial expressions, and body language. However, emotions expressed in such actions are sometimes manipulated by people and real feelings cannot be conveyed clearly. Therefore, understanding and analyzing emotions is essential. Recently, emotion analysis studies based on EEG signals appear to be in the foreground, due to the more reliable data collected. In this study, emotion analysis based on EEG signals was performed and a deep learning model was proposed. The study consists of four stages. In the first stage, EEG data were obtained from the GAMEEMO dataset. In the second stage, EEG signals were transformed with both VMD (variation mode decomposition) and EMD (empirical mode decomposition), and a total of 14 (nine from EMD, five from VMD) IMFs were obtained from each signal. In the third stage, statistical features were obtained from IMFs and maximum value, minimum value, and average values were used for this. In the last stage, both binary-class and multi-class classifications were made. The proposed deep learning model is compared with kNN (k nearest neighbor), SVM (support vector machines), and RF (random forest). At the end of the study, an accuracy of 70.89% in binary-class classification and 90.33% in multi-class classification was obtained with the proposed deep learning method.
Abdulrahman et al. (Thu,) conducted a other in Emotion recognition. Deep learning model with VMD and EMD signal transformation vs. kNN, SVM, and RF was evaluated on Classification accuracy. A proposed deep learning model for EEG-based emotion recognition achieved an accuracy of 70.89% in binary-class classification and 90.33% in multi-class classification.