Measure projection analysis: a probabilistic alternative to EEG independent component clustering
-
1
Swartz Center for Computational Neuroscience, UCSD, United States
-
2
ECE Dept, University of California San Diego, United States
A crucial step in the analysis of multi-subject and/or -session electroencephalographic (EEG) data using Independent Component Analysis (ICA) is combining information across multiple recordings from different subjects and/or sessions, each associated with its own set of independent component (IC) processes. A current approach is to create IC equivalence classes by clustering ICs based on information about their equivalent dipole locations, scalp-map topographies and mean event-related (or continuous) EEG measures, but there are several problems associated with IC clustering, such as its discontinuous nature, difficulty in proper statistical evaluation, and high sensitivity to clustering parameters (e.g., the number of clusters, the relative weights on different EEG measures, etc.). In this poster we introduce a novel probabilistic method, called Measure Projection Analysis (MPA) that overcomes these issues by abandoning the notion of distinct IC clusters. Instead, it searches voxel by voxel for brain regions having event-related IC process dynamics that exhibit statistically significant consistency across subjects and/or sessions as quantified by the values of various EEG measures. Local-mean EEG measure values are then assigned to all such locations based on a probabilistic model of IC localization error and inter-subject anatomical and functional differences.
Keywords:
brain oscillations,
EEG
Conference:
XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011.
Presentation Type:
Poster Presentation
Topic:
Poster Sessions: Quantitative Analysis of EEG, MEG & Brain Oscillations
Citation:
Bigdely-Shamlo
N,
Mullen
T,
Kreutz-Delgado
K and
Makeig
S
(2011). Measure projection analysis: a probabilistic alternative to EEG independent component clustering.
Conference Abstract:
XI International Conference on Cognitive Neuroscience (ICON XI).
doi: 10.3389/conf.fnhum.2011.207.00138
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
17 Nov 2011;
Published Online:
28 Nov 2011.
*
Correspondence:
Dr. Nima Bigdely-Shamlo, Swartz Center for Computational Neuroscience, UCSD, San Diego, United States, nima.bigdely@qusp.io