AbstractWhen EEG recordings are used to reveal interactions between central‐nervous and cardiovascular processes, the cardiac field artifact (CFA) poses a major challenge. Because the electric field generated by cardiac activity is also captured by scalp electrodes, the CFA arises as a heavy contaminant whenever EEG data are analyzed time‐locked to cardio‐electric events. A typical example is measuring stimulus‐evoked potentials elicited at different phases of the cardiac cycle. Here, we present a nonlinear regression method deploying neural networks that allows to remove the CFA from the EEG signal in such scenarios. We train neural network models to predict R‐peak centered EEG episodes based on the ECG and additional CFA‐related information. In a second step, these trained models are used to predict and consequently remove the CFA in EEG episodes containing visual stimulation occurring time‐locked to the ECG. We show that removing these predictions from the signal effectively removes the CFA without affecting the intertrial phase coherence of stimulus‐evoked activity. In addition, we provide the results of an extensive grid search suggesting a set of appropriate model hyperparameters. The proposed method offers a replicable way of removing the CFA on the single‐trial level, without affecting stimulus‐related variance occurring time‐locked to cardiac events. Disentangling the cardiac field artifact (CFA) from the EEG signal is a major challenge when investigating the neurocognitive impact of cardioafferent traffic by means of the EEG. When stimuli are presented time‐locked to the cardiac cycle, both sources of variance are systematically confounded. Here, we propose a regression‐based approach deploying neural network models to remove the CFA from the EEG. This approach effectively removes the CFA on a single‐trial level and is purely data‐driven, providing replicable results.