The paper introduces the PREP (Preprocessing for Robust EEG Processing) pipeline, a standardized and automated early-stage preprocessing pipeline for large-scale EEG analysis. The pipeline aims to improve the signal-to-noise ratio and reduce artifacts in EEG data, particularly for single-precision computations. It includes steps for line noise removal, robust referencing, and channel interpolation. The authors highlight the importance of standardization and automation in EEG data preparation, especially for large-scale machine learning applications. The PREP pipeline is designed to handle noisy channels and their interaction with referencing, using a multi-stage robust referencing scheme. The pipeline also provides detailed reporting and visualizations to help researchers identify and address issues in EEG datasets. The effectiveness of the PREP pipeline is demonstrated through various examples and comparisons with other methods, showing its ability to produce high-quality, standardized EEG data suitable for downstream analysis.The paper introduces the PREP (Preprocessing for Robust EEG Processing) pipeline, a standardized and automated early-stage preprocessing pipeline for large-scale EEG analysis. The pipeline aims to improve the signal-to-noise ratio and reduce artifacts in EEG data, particularly for single-precision computations. It includes steps for line noise removal, robust referencing, and channel interpolation. The authors highlight the importance of standardization and automation in EEG data preparation, especially for large-scale machine learning applications. The PREP pipeline is designed to handle noisy channels and their interaction with referencing, using a multi-stage robust referencing scheme. The pipeline also provides detailed reporting and visualizations to help researchers identify and address issues in EEG datasets. The effectiveness of the PREP pipeline is demonstrated through various examples and comparisons with other methods, showing its ability to produce high-quality, standardized EEG data suitable for downstream analysis.