Subject Independent Emotion Recognition Using Electroencephalogram Signals with Continuous Capsule Network Method - Made Agus Panji Sujaya; I Made Agus Wirawan; Gede Indrawan (JUITA: Jurnal Informatika, 2025)
Emotions play an essential role in human reasoning. Researchers have made various efforts to improve emotion classification methods. Based on the several emotion classification methods studied in previous studies, the Continuous Capsule Network method produced the highest accuracy compared to other classification methods. This method can maintain spatial information from electroencephalogram signals so they are not reduced. However, this method has only been tested subject-dependently. Based on this study, the Continuous Capsule Network method will be applied to classify emotions in the Faculty of Engineering and Vocational Studies, Universitas Pendidikan Ganesha students. The number of participants involved in this study was 17 people (10 men and 7 women). Through six subjectindependent test scenarios, the Continuous Capsule Network method produced accuracy, precision, recall, and F1 scores of 99.31%, 99.34%, 99.20%, and 99.27, respectively. At the same time, the loss value was 0.88%. In addition, the Continuous Capsule Network method produced an average training and validation time of 401.17 seconds and an average testing time of 4.67 seconds for the six test scenarios.