Semi-automatic identification of independent components representing EEG artifact.

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ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. (2011, background): “, 2009), lateral eye movements (Viola et al., 2009), heartbeat artifacts (Viola et al.”
Committee report: publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography. (2014, neutral): “A recent controversy in the hemodynamic neuroimaging literature (e.g., Kriegeskorte, Simmons, Bellgowan, & Baker, 2009; Lieberman, Berkman, & Wager, 2009; Viviani, 2010; Vul, Harris, Winkielman, & Pashler, 2009) applies as well to electromagnetic data.”
Robust artifactual independent component classification for BCI practitioners (2014, methodology): “As existing ICA-based plug-ins primarily focus on the detection of eye artifacts [27–29], we hope this will deliver a substantial contribution to the community by assisting EEG practitioners with the rejection of multiple type of artifacts.”
Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals (2011, background): “rely on spatial patterns only [19], or spatial patterns and spectral features [22], or spatial pattern and temporal features [20].”