Wearable brain-computer interfaces (BCIs) have made it feasible to monitor brain activity for emotion recognition in real-world settings. While deep learning models achieve high classification accuracy on EEG data, they often lack interpretability, limiting their neuroscientific relevance. In this study, we present an interpretable framework for EEG-based emotion analysis rooted in energy landscape analysis. EEG signals from the DEAP dataset were standardized and binarized prior to quantification of neural state transitions. We found significant subject-specific correlations between the number of state transitions and emotional ratings of valence and arousal. Further analysis revealed that certain binary brain states, particularly complementary pairs, were among the most frequently observed and showed emotion-dependent frequency differences. Transitions between these state pairs varied across subjects, suggesting their role as local minima in the brain’s dynamic landscape. Our findings demonstrate that energy landscape analysis provides an interpretable alternative to black-box models, offering insights into how brain dynamics relate to emotional experiences. This approach contributes toward building explainable affective computing systems and supports the use of neural state modeling in emotion-aware BCIs.
Fujiwara et al. (Fri,) studied this question.