|Authors:||Nima Bigdely-Shamlo, Tim Mullen, Christian Kothe(Intheon), Kyung-Min Su and Kay Robbins (UTSA)|
|Software Language(s):||MATLAB 2014 or later|
|Software Type:||command line / EEGLAB plugin with GUI|
What it does: PREP does automated early-stage preprocessing of EEG. Specifically, PREP removes line noise, computes a robust (artifact-independent) average reference, and identifies bad channels. PREP also produces an extensive report about signal quality.
Why is this important? EEG is very susceptible to artifacts and manual-based removal is time-consuming and highly subjective. Unfortunately, end-results are strongly influenced by this step. PREP standardizes and automates several baseline processing steps. We have demonstrated the effectiveness of this processing in large-scale analysis. SANDR uses PREP to produce its “level 2” processed data.
Bigdely-Shamlo N, Mullen T, Kothe C, Su K-M and Robbins KA (2015)
The PREP pipeline: standardized preprocessing for large-scale EEG analysis
Front. Neuroinform. 9:16. doi: 10.3389/fninf.2015.00016.
Recent publications that have used PREP in analysis:
Bigdely-Shamlo, N., Touryan, T., A. Ojeda, C. Kothe, T. Mullen, and K. Robbins, K., 2019. Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies, Neuroimage. 2019 Nov 23:116361.
Bigdely-Shamlo, N., Touryan, J., Ojeda, A., Kothe, C., Mullen, T., and Robbins, K., 2019. Automated EEG mega-analysis II: Cognitive aspects of event related features, Neuroimage. 2019 Sep 4:116054.
Robbins, K., Touryan, J. Mullen, T., Kothe, C. Bigdely-Shamlo, N., 2020. How sensitive are EEG results to preprocessing methods: A benchmarking study, IEEE Transactions on Neural Engineering and Rehabilitation. doi: 10.1109/TNSRE.2020.2980223.