Atrial fibrillation is the most prevalent sustained cardiac arrhythmia encountered by clinicians. This trouble is characterized by a persisting uncoordinated atrial electrical activation, which results in a inefficient atrial mechanical function and long-term risks of stroke. Despite its incidence and risks of serious complications, the electrophysiologicalmechanisms causing AF are not yet well understood. In clinical practice, AF is mainly diagnosed on the surface electrocardiogram (ECG), where the atrial and ventricular activities appear as a linear superposition of potential fields. This renders the evaluation of the atrial activity a complex task, and calls for the design of suitable signal processing techniques for atrial activity estimation in the ECG. This chapter presents some recent advances in the estimation of atrial activity exploiting the spatial diversity of the standard ECG. We first stress the relation between the electrophysiological activity of the heart and the potential field measured by the cutaneous electrodes, including the standard linear approximation of the bioelectrical field produced by a physiological source. This connection naturally leads to the notion of source topographies and spatial filtering. Given the surface electrode recordings, spatial filters aim to isolate the potential fields of the distinct sources by means of suitable linear combination of the lead outputs. We focus on how the estimation of spatial filters is typically handled in a blind or semi-blind context. This forms the basis for the presentation of recently proposed algorithms in the estimation of atrial activity. While benefiting from prior information about the atrial activity, the proposed algorithms keep their generality so that they can be used in a wide range of electrocardiogram recordings.