Methodological Taxonomy for Functional Brain–Heart Interplay Assessment: Creating a comprehensive taxonomy

The brain and heart have traditionally been investigated as distinct systems, with ad hoc signal processing methodologies tailored to their specific dynamics at the cortical, subcortical, and peripheral levels. However, increasing evidence highlights the fundamental role of brain–heart interplay (BHI), which can generate dynamics that neither system can produce in isolation. Through this interplay, impairments in one system can profoundly influence the other via complex neural, mechanical, and biochemical pathways. Consequently, there is a growing scientific interest in quantitatively characterizing BHI to better understand its functional dynamics and potential clinical implications. Focusing on the neural brain–heart axis as monitored through electroencephalographic and electrocardiographic signals, this study aims to systematically categorize existing signal processing methods for functional BHI assessment, thereby providing a comprehensive taxonomy from a methodological point of view. We show that BHI has been quantified using diverse analytical frameworks that leverage physiological specificity, mathematical modeling, and the ability to capture directional and time-varying interactions. Furthermore, we present a tutorial-like description on a physiologically inspired modeling approach that enables the estimation of BHI with high temporal resolution while preserving directional information. This study fosters the development of integrated approaches for BHI quantification, calling for collaboration among signal processing developers, neuroscientists, cardiologists, and computational physiologists.