Atrial fibrillation (AF) is one of the more common clinical arrhythmias with a high morbidity and mortality. Despite this, the electrophysiological and pathological mechanisms associated with AF largely remain a mystery, encouraging the use of ever more sophisticated techniques to extract vital information for diagnostic and therapeutic purposes. Contamination by signals of ventricular origin is considered the main artifact present in high-resolution epicardial electrograms (EGMs) that hinders the accurate and efficient analysis of AF EGM datasets. Furthermore, the complexity and dynamism of AF signals calls for robust data analysis tools that can effectively reduce or remove ventricular activity (VA) while preserving the texture and morphology of atrial activity (AA). Multiway component analysis, specifically block term decomposition (BTD), proves useful for the decontamination of epicardial EGMs as demonstrated in this project by enabling the automatic estimation of VA on an electrode-by-electrode basis, which is thereafter temporally and/or power spectrally subtracted thus retaining AA at a relatively high accuracy. The performance of BTD compared to average beat subtraction (ABS) and the more restrictive canonical polyadic decomposition (CPD) is visually verified and numerically confirmed based on a set of key performance indices. Additionally, the technique is entirely data-driven i.e., does not depend on any statistical properties, but if/when available, can contribute to enhanced performance via the imposition of appropriate constraints in the tensor decomposition.