Abstract : Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia but is still considered a challenging research subject since its electrophysiological mechanisms are not yet fully understood. Analyzing the atrial activity (AA) signal observed in surface electrocardiograms (ECG) is useful for clinical management and better understanding the propagation mechanisms inside the atria, but ventricular activity (VA) masks the AA in time and frequency domains. Signal processing techniques have been used to extract the AA signal. Blind Source Separation (BSS) methods can accomplish this task from multi-lead ECG. Recently, a deterministic tensor-based BSS method based on the Block Term Decomposition (BTD) was proposed and offered promising results in AA estimation. This method assumes that AF ECG leads can be expressed as linear combinations of damped exponential sources. However, QRST complexes of VA do not match this model, causing numerical issues. The present contribution proposes a Principal Component Analysis (PCA) preprocessing stage to attenuate the ventricular components. Experimental results show that this stage alleviates the ECG model mismatch, resulting in better AA estimation compared to competing methods and improved numerical properties com- pared to BTD without preprocessing.