http://www.nitrc.org/forum/forum.php?forum_id=2344 <p class="abstract"><div class="Abstract" lang="en"><a name="Abs1"></a><span class="AbstractHeading">Abstract&nbsp;&nbsp;</span><div class="normal">MEG and EEG measure electrophysiological activity in the brain with exquisite temporal resolution. Because of this unique strength relative to noninvasive hemodynamic-based measures (fMRI, PET), the complementary nature of hemodynamic and electrophysiological techniques is becoming more widely recognized (e.g., Human Connectome Project). However, the available analysis methods for solving the inverse problem for MEG and EEG have not been compared and standardized to the extent that they have for fMRI/PET. A number of factors, including the non-uniqueness of the solution to the inverse problem for MEG/EEG, have led to multiple analysis techniques which have not been tested on consistent datasets, making direct comparisons of techniques challenging (or impossible). Since each of the methods is known to have their own set of strengths and weaknesses, it would be beneficial to quantify them. Toward this end, we are announcing the establishment of a website containing an extensive series of realistic simulated data for testing purposes (<a href="http://cobre.mrn.org/megsim/">http://cobre.mrn.org/megsim/</a>). Here, we present: 1) a brief overview of the basic types of inverse procedures; 2) the rationale and description of the testbed created; and 3) cases emphasizing functional connectivity (e.g., oscillatory activity) suitable for a wide assortment of analyses including independent component analysis (ICA), Granger Causality/Directed transfer function, and single-trial analysis. </div> </div></p><ul> <li><span class="labelName">Content Type </span><span class="labelValue">Journal Article</span></li><li>Category Original Article</li><li>Pages 1-18</li><li>DOI 10.1007/s12021-011-9132-z</li><li><span class="labelName">Authors</span><ul> <li>C. J. Aine, Department of Radiology, MSC10 5530, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA</li><li>L. Sanfratello, Department of Radiology, MSC10 5530, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA</li><li>D. Ranken, Los Alamos National Laboratory, Los Alamos, NM, USA</li><li>E. Best, The Mind Research Network, Albuquerque, NM, USA</li><li>J. A. MacArthur, The Mind Research Network, Albuquerque, NM, USA</li><li>T. Wallace, Department of Radiology, MSC10 5530, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA</li><li>K. Gilliam, The Mind Research Network, Albuquerque, NM, USA</li><li>C. H. Donahue, Department of Radiology, MSC10 5530, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA</li><li>R. Montaño, Department of Radiology, MSC10 5530, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA</li><li>J. E. Bryant, Department of Radiology, MSC10 5530, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA</li><li>A. Scott, The Mind Research Network, Albuquerque, NM, USA</li><li>J. M. Stephen, The Mind Research Network, Albuquerque, NM, USA</li> </ul></li> </ul><ul class="parents"> <ul class="details"> <li><span class="header labelName">Journal </span><span class="labelValue"><a href="http://www.springerlink.com/content/120559/">Neuroinformatics</a></span></li><li><span class="labelName">Online ISSN </span><span class="labelValue">1559-0089</span></li><li><span class="labelName">Print ISSN </span><span class="labelValue">1539-2791</span></li> </ul> </ul> en-us Copyright 2000-2026 NITRC OSI Mon, 20 Apr 2026 1:22:50 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator