EEG-based functional and effective connectivity patterns during emotional episodes using graph theoretical analysis - Majid Roshanaei; Hamzeh Norouzi; Julie Onton; Scott Makeig; Alireza Mohammadi (Scientific Reports)
Abstract Understanding the neural mechanisms underlying emotional processing is critical for advancing neuroscience and mental health interventions. This study examined these mechanisms by analyzing EEG connectivity patterns across different brain regions while participants evoked various emotions. After applying independent component analysis (ICA) to eliminate non-cortical activity, we assessed frequency-specific connectivity patterns using coherence, Granger causality, and graph theoretical measures to evaluate both functional and effective connectivity. Graph theoretical analysis revealed significant differences in connectivity between emotions across multiple frequency bands, particularly in the delta and beta bands. These results indicated modulations in key brain regions, such as the precentral, superior frontal, and temporal areas, suggesting that these regions play a crucial role in emotional processing. Coherence analysis demonstrated predominant alpha band activity across all emotions, with specific emotional states, such as fear, grief, and jealousy, exhibiting enhanced beta band activity. In terms of coherence strength, we observed that the gamma band was largely inactive, except for the emotion of sadness, which displayed significant activity in the right lobe, particularly in regions such as the supplementary motor area and the lingual gyrus. Additionally, Granger causality analysis highlighted that the beta and gamma bands were dominant across all emotional states, with minimal modulation observed in the theta band. Clustering coefficients from the graph analysis further revealed distinct patterns of connectivity in the delta and beta bands, with significant variations across different emotions, particularly in the temporal and frontal regions. These findings enhance our understanding of emotional processing and have practical applications in mental health, biomarker identification, and human-computer interaction.