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

By Nima Bigdely Shamlo, T. Mullen, Christian Andreas Kothe, Kyungmin Su, K. Robbins. Front. Neuroinform., 2015

1,228 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/2810095d227e3c547e9d1855e20ec4d6ddcea0ad

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In Cognitive Neuroscience

Citations in the corpus

Deep learning-based electroencephalography analysis: a systematic review (2019, background): “In many fields of application of DL, input data usually has a high SNR: in both CV and NLP, for instance, virtually noise-free images and natural language excerpts are easy to obtain.”
Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open‐source TESA software (2017, neutral): “…introduced to TMS–EEG recordings during analysis Typical analysis pipelines for event-related potential EEG studies include several steps such as filtering, downsampling, epoching, baseline-correcting, ICA and finally averaging the data across epochs (Bigdely-Shamlo et al., 2015; Luck, 2005).”
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website (2019, methodology): “This problem can be mitigated through adequate signal preprocessing prior to applying ICA and, as many effective preprocessing pipelines already exist (Bigdely-Shamlo et al., 2015; Mullen et al., 2013), this work does not address preprocessing.”
Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG (2015, neutral): “, Xc above) using the RANSAC method presented in [29].”
The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data (2018, methodology): “…et al., 2014, SASICA, Chaumon et al., 2015), but they have largely been developed and tested on healthy adult EEG data with low levels of artifact contamination (Nolan et al., 2010; Mognon et al., 2011; Mullen et al., 2013; Bigdely-Shamlo et al., 2015; Chaumon et al., 2015; Hatz et al., 2015).”
Data Augmentation for Deep-Learning-Based Electroencephalography. (2020, methodology): “EEG also suffers from low signal-to-noise ratio (SNR) [1-4].”
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2016, methodology): “Due to extensive signal noise present in the data, the EEG data were first processed with the PREP pipeline [61].”
RELAX‐Jr: An Automated Pre‐Processing Pipeline for Developmental EEG Recordings (2024, unknown): “By default, RELAX-Jr receives as inputs raw datafiles in EEGLAB format (Delorme and Makeig 2004), and outputs cleaned continuous data referenced to the robust average reference ready for further segmentation (optional) and analysis (Bigdely-Shamlo et al. 2015).”
Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood (2021, methodology): “…filtered between 0.5 – 80 Hz (fourth-order Butterworth filter), with a notch filter between 47-53 Hz to remove any line noise, following which any bad channels were removed using a multi-step process including the ‘findNoisyChannels’ function from the PREP pipeline (Bigdely-Shamlo et al., 2015).”