The electroencephalogram (EEG) is a non-invasive and low-cost tool for the investigation of human brain function. However, EEG data are typically contaminated with a number of artifacts. With the advent of both high-density EEG arrays and studies of large populations, yielding increasingly greater amounts of data, supervised methods for artifact rejection have become excessively time consuming. To cope with this, and to minimize subjectivity, automatic methods have recently been presented (Hatz et al., 2015; Nolan, Whelan, & Reilly, 2010). Here, we propose a novel automatic pipeline for pre-processing and artifact rejection of EEG data (both evoked-related potentials (ERPs) and resting-state (RS) data) based on state-of-the-art guidelines and robust statistics.