This is SASICA, a plugin to EEGlab to help you reject/select independent components based on various properties of these components.
Available methods are:
Autocorrelation: detects noisy components with weak
autocorrelation (muscle artifacts usually)
Focal components: detects components that are too focal and
thus unlikely to correspond to neural
activity (bad channel or muscle usually).
Focal trial activity: detects components with focal trial
activity, with same algorhithm as focal
components above. Results similar to trial
variability.
Signal to noise ratio: detects components with weak signal
to noise ratio between arbitrary baseline
and interest time windows.
Dipole fit residual variance: detects components with high
residual variance after subtraction of the
forward dipole model. Note that the inverse
dipole modeling using DIPFIT2 in EEGLAB
must have been computed to use this
measure.
EOG correlation: detects components whose time course
correlates with EOG channels.
Bad channel correlation: detects components whose time course
correlates with any channel(s).
ADJUST selection: use ADJUST routines to select components
(see Mognon, A., Jovicich, J., Bruzzone,
L., & Buiatti, M. (2011). ADJUST: An
automatic EEG artifact detector based on
the joint use of spatial and temporal
features. Psychophysiology, 48(2), 229-240.
doi:10.1111/j.1469-8986.2010.01061.x)
FASTER selection: use FASTER routines to select components
(see Nolan, H., Whelan, R., & Reilly, R. B.
(2010). FASTER: Fully Automated Statistical
Thresholding for EEG artifact Rejection.
Journal of Neuroscience Methods, 192(1),
152-162. doi:16/j.jneumeth.2010.07.015)
MARA selection: use MARA classification engine to select components
(see Winkler I, Haufe S, Tangermann M.
2011. Automatic Classification of
Artifactual ICA-Components for Artifact
Removal in EEG Signals. Behavioral and
Brain Functions. 7:30.)
If you use this program in your research, please cite the following
article:
Chaumon M, Bishop DV, Busch NA. A Practical Guide to the Selection of
Independent Components of the Electroencephalogram for Artifact
Correction. Journal of neuroscience methods. 2015
SASICA is a software that helps select independent components of
the electroencephalogram based on various signal measures.
Copyright (C) 2014 Maximilien Chaumon
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Some of the measures used here are based on http://bishoptechbits.blogspot.com/2011/05/automated-removal-of-independent.html