This is SASICA, a tool to help you reject/select independent components based on various properties of these components.
This is a pre-release of the FieldTrip compatible version of SASICA (originally for EEGLAB). Please report any bug to max.chaumon@gmail.com
In your favorite directory with git clone -b feature/ft_compat https://github.com/dnacombo/SASICA.git
or download the zip file here and uncompress in your favorite directory.
Then in MATLAB: addpath('your/favorite/directory')
[cfg] = ft_SASICA(cfg,comp,data)
with inputs:
- cfg: a structure with field
layout: that will be passed to ft_prepare_layout
as well as any of the following fields (see below for explanations):
autocorr detect components with low autocorrelation
focalcomp detect focal components in sensor space
trialfoc detect focal components in trial space
SNR detect components with low signal to noise
ratio across trials between two time windows.
EOGcorr detect components with high correlation with
vertical and horizontal EOG channels
chancorr detect components with high correlation with
any channel
FASTER use FASTER (Nolan et al. 2010) detection
methods.
ADJUST use ADJUST (Mongon et al. 2011) detection
methods
opts set various options: noplot, nocompute, FontSize
- comp: the output of ft_componentanalysis
- data: the output of ft_preprocessing
For more detailed information, see doc eeg_SASICA
For an example cfg structure, run cfg = ft_SASICA('getdefs')
- 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.
- 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)
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
Copyright (C) 2019 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