Understanding the dynamic interaction between the amygdala and the ventromedial prefrontal cortex (vmPFC) is essential for unraveling the neural mechanisms underlying emotion regulation. This study introduces a multidimensional analytical framework that integrates functional connectivity, time-lagged correlation, Granger causality analysis (GCA), and entropy-based complexity metrics to explore the amygdala–vmPFC relationship during emotionally aversive tasks using intracranial EEG (iEEG) data. Our findings reveal a significant negative functional correlation (r = -0.009, p < 0.05) between the amygdala and vmPFC, indicating an inhibitory relationship. Time-lagged correlation analysis further uncovers a temporal delay (~30 time points), suggesting that amygdala activity precedes vmPFC modulation. Entropy analysis shows that the vmPFC exhibits higher signal complexity (H = 4.26) than the amygdala (H = 3.98), consistent with its role in higher-order emotional regulation. However, GCA results yielded no statistically significant directional influence, highlighting the non-linear and multifaceted nature of these interactions. This study is among the first to combine entropy, time-lag, and causality metrics for analyzing vmPFC–amygdala dynamics in human iEEG. Our integrated framework offers deeper insights into brain region interplay and lays the groundwork for future research in affective neuroscience and neuroeconomics.