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README.md

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 # eeg_commander
-MATLAB pipeline for easy-to-program automated pre-processing of electroencephalogram (EEG) data using independent component analysis and robust artifact thresholding.
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+This is a MATLAB pipeline for easy-to-program automated pre-processing of electroencephalogram (EEG) data using independent component analysis (ICA) and robust artifact thresholding. It is intended to work similar to a plug-in for the EEGLAB (https://sccn.ucsd.edu/eeglab/index.php) software suite, but it additionally contains low-level functions that work on simpler data arrays (e.g., 2- or 3-dimensional matrices) that may be useful to users. Generally, the EEG pipeline is designed to successively prune EEG artifacts identified in 1) one-second contiguous segments in the continuous data, in 2) grossly bad data channels, in 3) spatiotemporal stereotyped ICA artifacts, and in 4) channel/time-segments (e.g., trial epochs). The toolbox was originally developed by Scott Burwell and Steve Malone at the University of Minnesota in 2011, and explained further in: Burwell, S. J., Malone, S. M., Bernat, E. M., & Iacono, W. G. (2014). Does electroencephalogram phase variability account for reduced P3 brain potential in externalizing disorders?. Clinical Neurophysiology, 125(10), 2007-2015. 
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+Notable features of the EEG pipeline include: 
+* Independent component analysis spatiotemporal artifact identification and removal
+* Robust statistical approaches for identification of artifacts, including
+    * joint statistical threshold identification of artifacts (e.g., Mognon et al., 2010)
+    * statistical thresholding using the normalized median absolute deviation (cf. Rousseeuw & Croux, 1993)
+* Identification and interpolation of channel or channel/time-segment artifacts (e.g., Nolan et al., 2010; Junghofer et al., 2000)
+* Easy-to-program modular automation via proc_commander(), a tool that takes arguments similar to SPM's spm_jobman() (cf. https://en.wikibooks.org/wiki/SPM/Batch)
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+Additional useful low-level functions:
+* Identification of salt-bridge artifacts based on the electrical distance (Tenke et al., 2001), see get_elecdists()
+* Removal of muscle-artifacts (i.e., white-noise signals) based on canonical correlation (De Clercq et al., 2006), see bsscca_correct_emg()
+* Easy-to-implement Current Source Density transformation (http://psychophysiology.cpmc.columbia.edu/Software/CSDtoolbox/), see proc_CSD()
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