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| There are many available; here are some (some comments may be ignorant!) | |
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| == SPM5 == | This is a list of freeware packages more frequently used at the CBU: == SPM == |
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| * Much experience at CBU, integrated with fMRI analysis | * Much experience at CBU, fully integrated with fMRI analysis * Unique strength in image statistics, eg Random Field Theory correction for Sensor-Time-Frequency images * Fully flexible GLM, for complex designs (e.g, parametric modulation of evoked responses per trial) * Novel variants of the L2-norm approach to the inverse problem (eg group inversion, EEG+MEG+fMRI fusion, canonical meshes in MNI space) |
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| * Advanced statistical methods (e.g, for multiple comparisons across space or time), * Novel variants of the L2-norm approach to the inverse problem * Group-based analysis (eg in MNI/Talairach space). |
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| * Slow graphics * ECD currently limited |
* Slowish graphics * Limited head modelling (uses inverse-normalised template meshes) Note there are two versions: SPM5 and SPM8. SPM8 is better in many ways, and should be used by people new to SPM. Prior development at the CBU with SPM5 however means that there is more support in terms of batch scripts etc. We hope these will be updated for SPM8 over the next year. The main advantages of SPM8 over SPM5 are: * Fully compatible with Fieldtrip and EEGLAB (shared fileio, including the MNE Matlab functions to read FIF data; see below) * Better GUI (fast enough to examine raw data) * Better forward modelling (eg BEMs, using Fieldtrip code) * DCM for evoked, induced and phase-coupling * Bayesian dipole fitting |
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| Currently installed on all Linux Boxes (lowest load box selected by ''spm 5 eeg'' at linux prompt) | Currently installed on all Linux Boxes (lowest load box selected by ''spm 5 eeg'' or ''spm 8 eeg'' at linux prompt) |
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| * Advanced distributed solution methods (mainly by Matti Hämäläinen, a long-standing expert with strong links to Neuromag) | * Minimum Norm Estimation relying on minimal modeling assumptions, i.e. applicable to complex source configurations and high noise levels (by Matti Hämäläinen, a long-standing expert with strong links to Neuromag) |
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| * Matlab Toolbox for exporting and importing data (though this is independently available) * Comprehensive documentation |
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| * Compiled code, so difficult to extend | * Compiled code, so difficult to extend (though it does come with an extensive MATLAB toolbox, which is arguably the best way to read FIF data). * No statistics and time-frequency analysis (yet) |
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| Soon to be installed on Linux Boxes | Installed on Linux Boxes. All Graphical tools must be used in the computing room on lws001, lws002, lws004, lws005, lws006 or using a vncserver.glx session (see http://imaging.mrc-cbu.cam.ac.uk/imaging/ImagingComputingDevelopments). For more details, see here MneAnalysis |
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| == Multiple Linear Regression of EEG/MEG data == Matlab tools for applying multiple linear regression to EEG/MEG data. More here: LinearRegression You may want to follow the following AnalysisPath == Data Screening Tool == Matlab utilities for quality control of your data prior to further processing. More here: DataScreeiningTool |
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| * FIFFACCESS for simply reading FIF data into Matlab, more here: ReadingDataInMatlab |
MEG Freeware Packages
This is a list of freeware packages more frequently used at the CBU:
SPM
Advantages:
- Much experience at CBU, fully integrated with fMRI analysis
- Unique strength in image statistics, eg Random Field Theory correction for Sensor-Time-Frequency images
- Fully flexible GLM, for complex designs (e.g, parametric modulation of evoked responses per trial)
- Novel variants of the L2-norm approach to the inverse problem (eg group inversion, EEG+MEG+fMRI fusion, canonical meshes in MNI space)
- Matlab, so readable and extendable
Disadvantages:
- Slowish graphics
- Limited head modelling (uses inverse-normalised template meshes)
Note there are two versions: SPM5 and SPM8. SPM8 is better in many ways, and should be used by people new to SPM. Prior development at the CBU with SPM5 however means that there is more support in terms of batch scripts etc. We hope these will be updated for SPM8 over the next year. The main advantages of SPM8 over SPM5 are:
- Fully compatible with Fieldtrip and EEGLAB (shared fileio, including the MNE Matlab functions to read FIF data; see below)
- Better GUI (fast enough to examine raw data)
- Better forward modelling (eg BEMs, using Fieldtrip code)
- DCM for evoked, induced and phase-coupling
- Bayesian dipole fitting
Official website: http://www.fil.ion.ucl.ac.uk/spm/
Currently installed on all Linux Boxes (lowest load box selected by spm 5 eeg or spm 8 eeg at linux prompt)
For more details, see here SpmAnalysis
MNE (Minimum Norm Estimates)
Advantages:
- Minimum Norm Estimation relying on minimal modeling assumptions, i.e. applicable to complex source configurations and high noise levels (by Matti Hämäläinen, a long-standing expert with strong links to Neuromag)
- Excellent, fast graphics
Linked with Free Surfer http://imaging.mrc-cbu.cam.ac.uk/imaging/FreesurferInformation (advanced software for MRI segmentation, extraction of cortical surfaces, and inflation/normalisation)
- Matlab Toolbox for exporting and importing data (though this is independently available)
- Comprehensive documentation
Disadvantages:
- Compiled code, so difficult to extend (though it does come with an extensive MATLAB toolbox, which is arguably the best way to read FIF data).
- No statistics and time-frequency analysis (yet)
Official website: http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php
Installed on Linux Boxes. All Graphical tools must be used in the computing room on lws001, lws002, lws004, lws005, lws006 or using a vncserver.glx session (see http://imaging.mrc-cbu.cam.ac.uk/imaging/ImagingComputingDevelopments).
For more details, see here MneAnalysis
FieldTrip
Advantages:
- Strong in oscillatory analysis (time-frequency, coherence) and beamformers (eg DICS)
- In Matlab, so easily readable and extendable
Official website: http://www.ru.nl/fcdonders/fieldtrip/
Multiple Linear Regression of EEG/MEG data
Matlab tools for applying multiple linear regression to EEG/MEG data. More here: LinearRegression You may want to follow the following AnalysisPath
Data Screening Tool
Matlab utilities for quality control of your data prior to further processing. More here: DataScreeiningTool
Yet Others
FIFFACCESS for simply reading FIF data into Matlab, more here: ReadingDataInMatlab
EEGLAB (good for ICA) http://www.sccn.ucsd.edu/eeglab/
Brainstorm (good for forward models) http://neuroimage.usc.edu/brainstorm/
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