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Kevin Tan's EEGLAB pipeline performs ICA-based EEG preprocessing and source localization. The pipeline is designed for ERP analyses, but can be modified for almost any M/EEG analyses, such as ERSP and single-trial classification. Preprocessing is fully-automated, while source localization can be fully- or semi-automated.
Extensive algorithmic and theoretical description can be found on the pages to the right. Code can be downloaded here for illustrative purposes only – not actively maintained!
Epoched EEG data before (left) and after (right) artifactual IC subtraction in Stage 3 of the pipeline. IC subtraction has cleaned ocular, muscle, and other artifacts from the data.
The pipeline contains three stages:
- Stage 1 – PREP for early-stage preprocessing
- Stage 2 – ICA decomposition and source localization
- Stage 3 – Final preprocessing that readies data for further analyses
Use of this pipeline in a source-space ERP paradigm can be found in Tan & Tarr (2016).
This guide and its code are tailored for .bdf recordings from Carnegie Mellon Psychology's 128-channel BioSemi. It has been tested on CNBC's Psych-O cluster, using Matlab 2013a (should work up to Matlab 2014a).
This pipeline is very computationally intensive, and requires the use of an HPC cluster. Using 36 threads, a 1hr EEG recording (136ch @ 512Hz) takes 12hr+ to complete. For faster preprocessing, there are less intensive pipelines available.
This pipeline was developed with input from Ying Yang and Michael Tarr at Carnegie Mellon, Makoto Miyakoshi and Jason Palmer at UCSD, and various members of the EEGLAB mailing list.
Many ideas here were derived from Makoto's pipeline and EEGLAB documentation. Check them out!
Use this pipeline at your own risk! The author makes no claims or guarantees related to the content herein. The author is not liable for any unfavorable outcomes that may result.