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

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.

Publication DOI: https://doi.org/10.1155/2011/758973
Divisions: College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences
Additional Information: Copyright © 2011 Sarang S. Dalal et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Full Text Link:
Related URLs: https://www.hin ... in/2011/758973/ (Publisher URL)
PURE Output Type: Article
Published Date: 2011-03
Authors: Dalal, SS
Zumer, JM (ORCID Profile 0000-0003-0419-3869)
Guggisberg, AG
Trumpis, M
Wong, DD
Sekihara, K
Nagarajan, SS

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