Exploring the Relationship between EEG Features of Basic and Academic Emotions

Abstract

This study aimed to explore the relationship between basic and academic emotions by analyzing their EEG patterns. Using MAHNOB-HCI (MH) and Academic Emotion (AE) datasets, we performed three experiments based on valence and discrete emotion models. Our analysis revealed no similarity between the valence of basic and academic emotion datasets. However, we found that three out of 84 features in the MH discrete emotion dataset had a statistically significant relationship with the AE frustration dataset, suggesting some commonality between basic and academic emotions, particularly in the case of frustration. We also used random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM) models to validate our findings, with the RF model outperforming the others in terms of valence classification accuracy. Our study provides valuable insights into the relationship between basic and academic emotions and may inform future research in this area.