nickketz / ft_sasica Goto Github PK

porting of eeglab plugin 'SASICA' to fieldtrip.  Please see https://github.com/dnacombo/SASICA for original code

currently only works on continous fieldtrip data

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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