INFR-GC: Interpretable Feature Representations for Granger Causality in Cortico-muscular Interactions

Understanding the interactions between the central nervous system and muscular responses is essential for developing effective strategies to diagnose and manage movement disorders such as dystonia. This study addresses these complex interactions by introducing a novel non-linear forecasting method for time series data. We propose that mutual information, by detecting complex dependencies between time series, can uncover hidden relationships suggestive of Granger causality, thereby enhancing the scope and precision of causality analysis. Our approach emphasizes the selection of the most informative features for predicting the target variable through iterative extraction and evaluation. We employ an optimized gradient-boosted random forest algorithm, prioritizing features with the highest mutual information relative to the target variable. Additionally, a Granger causality metric, tailored for non-linear models, is developed to quantify the strength of the discovered interactions. Experimental validation on real physiological data demonstrates the effectiveness of our method in uncovering causal relationships and assessing feature importance, contributing to a deeper understanding of movement control mechanisms.