A practical guide to the selection of independent components of the electroencephalogram for artifact correction.

By Maximilien Chaumon, D. V. Bishop, Niko A. Busch. Journal of Neuroscience Methods, 2015

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The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data (2018, background): “…FASTER, Nolan et al., 2010, ADJUST, Mognon et al., 2011, TAPEEG, Hatz et al., 2015, ASR, Mullen et al., 2013, MARA, Winkler 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…”
Get ready! High urgency reduces beta band cortico-muscular coherence during motor preparation (2024, unknown): “EEG data were processed offline in MATLAB 2018a using EEGLAB (Delorme and Makeig, 2004), clean_rawdata, PREP Pipeline, AMICA (Palmer et al., 2012), and SASICA (Chaumon et al., 2015) plugins.”
Neural correlates of processing case in adults and children. (2025, unknown): “The SASICA and FASTER algorithms (Chaumon, Bishop, & Busch, 2015 ; Nolan, Whelan, & Reilly, 2010) were used to identify artifactual independent components.”