Software for biomedical applications
The EEGLab toolbox (2012)
Contributed by Arnaud Delorme
Programming language: Matlab and C
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This growing toolbox provides an extensive open source Matlab environment for exploratory and routine signal processing and visualization of electrophysiological data. It includes Scott Makeig’s Matlab runica implmentation of extended infomax ICA by Bell & Sejnowski and Te-Won? Lee, as well as a translation of runica into C by Sigurt Enghoff. An extensive web tutorial and reference paper is also available: Delorme & Makeig (2004), J. Neurosci Meth, 134(1), 9-21. Lvalist users and others are invited to write and distribute simple Matlab plug-in functions making their interoperable Matlab functions appear in the EEGLAB GUI menu.
ICA for image analysis, including spatial, temporal and spatiotemporal ICA (2001)
Contributed by Jim Stone
Programming language: Matlab 5.2
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This program implements algorithms presented in a tehnical report “Regularisation Using Spatiotemporal Independence and Predictability” (JV Stone and J Porrill, Computational Neuroscience Technical Report number 1, Psychology Department, Sheffield University). The program is set up to process image sequences (e.g. fMRI data), but could be adapted for other data. The program can be run in several modes:
- Spatial ICA: This decomposes an image sequence into a set of spatially independent images and a corresponding set of dual temporal signals.
- Temporal ICA: This decomposes an image sequence into a set of temporally independent time courses and a corresponding set of dual spatial images.
- Spatiotemporal ICA: This decomposes an image sequence into a set of spatial images and a corresponding set of time courses such that signals in both sets are maximally independent.
- Weak Model ICA: This regularises solutions found by ICA. The form of weak model used here assumes that underlying sources signals or their dual signals vary smoothly over time. This can be shown to improve the nature of solutions found by ICA.
Group ICA Of fMRI Toolbox (GIFT)
Contributed by Srinivas Rachakonda
Programming language: MATLAB
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Group ICA of fMRI Toolbox (GIFT) is a MATLAB toolbox which implements multiple algorithms for independent component analysis and blind source separation of group (and single subject) functional magnetic resonance imaging data. GIFT is based on paper V. Calhoun et al., A Method for Making Group Inferences From Functional MRI Data Using Independent Component Analysis Hum.Brain Map., vol. 14, pp. 140-151, 2001. Please see http://mialab.mrn.org/software/gift/index.html for more information.
Group ICA Of EEG Toolbox (EEGIFT)
Contributed by Srinivas Rachakonda
Programming language: MATLAB
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Group ICA of EEG Toolbox (EEGIFT) is a MATLAB toolbox which implements temporal independent component analysis of group (and single subject) electro encephalogram data. Please see http://mialab.mrn.org/software/eegift/index.html for more information.
Fusion ICA Toolbox (FIT)
Contributed by Srinivas Rachakonda
Programming language: MATLAB
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Fusion ICA Toolbox (FIT) is a Matlab toolbox used to examine the shared information across different modalities like fMRI, EEG, Gene, etc. Currently joint ICA, parallel ICA and CCA-Joint ICA methods are implemented in the FIT toolbox. Please see http://mialab.mrn.org/software/fit/index.html for more information.