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MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG.

Dalal, Sarang S
Zumer, Johanna M
Trumpis, Michael
Wong, Daniel D E
Sekihara, Kensuke
Nagarajan, Srikantan S
Published in Computational Intelligence and Neuroscience. 2011, vol. 2011, 758973
Abstract NUTMEG is a source analysis toolbox geared towards cognitive neuroscience researchers using MEG and EEG, including intracranial recordings. Evoked and unaveraged data can be imported to the toolbox for source analysis in either the time or time-frequency domains. NUTMEG offers several variants of adaptive beamformers, probabilistic reconstruction algorithms, as well as minimum-norm techniques to generate functional maps of spatiotemporal neural source activity. Lead fields can be calculated from single and overlapping sphere head models or imported from other software. Group averages and statistics can be calculated as well. In addition to data analysis tools, NUTMEG provides a unique and intuitive graphical interface for visualization of results. Source analyses can be superimposed onto a structural MRI or headshape to provide a convenient visual correspondence to anatomy. These results can also be navigated interactively, with the spatial maps and source time series or spectrogram linked accordingly. Animations can be generated to view the evolution of neural activity over time. NUTMEG can also display brain renderings and perform spatial normalization of functional maps using SPM's engine. As a MATLAB package, the end user may easily link with other toolboxes or add customized functions.
Keywords Bayes TheoremBrain/physiologyBrain MappingElectroencephalographyHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMagnetoencephalographyModels, NeurologicalNonlinear DynamicsSoftwareStatistics, Nonparametric
PMID: 21437174
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Article (Published version) (3.6 MB) - public document Free access
Research group Neuroréhabilitation assistée par imagerie (910)
FNS: 320030-129679
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DALAL, Sarang S et al. MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG. In: Computational Intelligence and Neuroscience, 2011, vol. 2011, p. 758973. doi: 10.1155/2011/758973 https://archive-ouverte.unige.ch/unige:25269

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Deposited on : 2013-01-09

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