The PREP pipeline: standardized preprocessing for large-scale EEG analysis

TLDR

The rapid portability of brain‑imaging technology enables large‑scale real‑world EEG analysis, but the resulting data are large and complex, making automated preprocessing essential. The study shows that neglecting early EEG preprocessing degrades signal quality and introduces artifacts, and proposes the PREP pipeline—a multi‑stage robust referencing scheme—to standardize early‑stage processing for large‑scale EEG datasets. The PREP pipeline implements a multi‑stage robust referencing scheme, automatically generates a report for each dataset, and is available as a free MATLAB library at eegstudy.org/prepcode. The authors find that while ordinary average referencing improves signal‑to‑noise ratio, noisy channels can still contaminate results, and that noisy‑channel identification depends on the reference, highlighting a complex interaction among filtering, channel identification, and referencing.

Abstract

The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.