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fastICA specifications

Information


Unique identifier OMICS_19692
Name fastICA
Alternative name fast Independent Component Analysis
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages C, R
License GNU General Public License version 3.0
Computer skills Advanced
Version 1.2-1
Stability Stable
Requirements
MASS, R(≥3.0.0)
Source code URL https://cran.r-project.org/src/contrib/fastICA_1.2-1.tar.gz
Maintained Yes

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Documentation


Maintainers


  • person_outline Jonathan Marchini
  • person_outline Ella Bingham
  • person_outline Aapo Hyvarinen
  • person_outline Brian Ripley
  • person_outline Chris Heaton

Publications for fast Independent Component Analysis

fastICA citations

 (310)
library_books

DCT Based Preprocessing Approach for ICA in Hyperspectral Data Analysis

2018
PMCID: 5948902
PMID: 29642496
DOI: 10.3390/s18041138

[…] non-Gaussianity by measuring the distance from normality; however, it is difficult to be computed. Hyvärinen and Oja proposed in [] an approximate formula, that gives birth to the algorithm known as FastICA [] .As a statistical technique, FastICA approach results rely on the initialization conditions, the parameterizations of the algorithm and the sampling of the dataset []. Therefore, the result […]

library_books

Adaptive cortical parcellations for source reconstructed EEG/MEG connectomes

2018
Neuroimage
PMCID: 5864515
PMID: 28893608
DOI: 10.1016/j.neuroimage.2017.09.009

[…] surfer.nmr.mgh.harvard.edu/) and EEG/MEG analyses were performed in the MNE python software package (version 0.9) http://martinos.org/mne/stable/mne-python.html). The ICA analysis was performed using FastICA algorithm () as included in scikit-learn python package () and implemented in MNE-Python meeg-preprocessing package. As the first step, the dimensionality of the data was reduced using princip […]

library_books

Estimation of Muscle Force Based on Neural Drive in a Hemispheric Stroke Survivor

2018
PMCID: 5876305
PMID: 29628911
DOI: 10.3389/fneur.2018.00187

[…] Raw EMG signals were decomposed into individual MU discharge events using the FastICA method () that has been verified as an accurate decomposition algorithm by previous studies (). All the details of the decomposition algorithm and the parameter selection have been described i […]

call_split

Electroencephalographic derived network differences in Lewy body dementia compared to Alzheimer’s disease patients

2018
Sci Rep
PMCID: 5854590
PMID: 29545639
DOI: 10.1038/s41598-018-22984-5
call_split See protocol

[…] ilter. 2) Bad channels were deleted including the reference electrode Fz; mean of four channels, with minimum one and maximums of 10 channels deleted. 3) Independent component analysis (ICA) with the FastICA algorithm using default parameters was run for the entire EEG recording and up to 12 artefactual independent components were deleted. At this step special interest was put on identifying eye b […]

library_books

Investigating Patterns for Self Induced Emotion Recognition from EEG Signals

2018
PMCID: 5877378
PMID: 29534515
DOI: 10.3390/s18030841

[…] cy interferences were filtered out from EEG signals by using a band-pass filter with a range of 0.1–80 Hz. Then, electrooculography (EOG) artifacts were removed by the blind-source analysis algorithm FastICA []. Each subject’s signal was decomposed into 61 independent components (ICs). Then, EOG artifacts were selected and removed. a,b illustrate EEG data before and after the removal of EOG artifa […]

call_split

Event Related Potential Responses to Task Switching Are Sensitive to Choice of Spatial Filter

2018
Front Neurosci
PMCID: 5852402
PMID: 29568260
DOI: 10.3389/fnins.2018.00143
call_split See protocol

[…] all-repeat) epochs were extracted from −1,000 to 3,500 ms with respect to cue onset. To remove blink and vertical eye-movement artifact, independent components analysis (ICA) was performed using the fastICA algorithm, (Hyvärinen and Oja, ). This produces a set of components, one less than the amount of available electrodes. Based on visual inspection by a trained observer, 1.40 ± 0.80 components […]

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fastICA institution(s)
Neural Networks Research Centre, Helsinki University of Technology, Espoo, Finland

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