Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience / 2007 / Article
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EEG/MEG Signal Processing

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Research Article | Open Access

Volume 2007 |Article ID 014567 | https://doi.org/10.1155/2007/14567

Gwen A. Frishkoff, Robert M. Frank, Jiawei Rong, Dejing Dou, Joseph Dien, Laura K. Halderman, "A Framework to Support Automated Classification and Labeling of Brain Electromagnetic Patterns", Computational Intelligence and Neuroscience, vol. 2007, Article ID 014567, 13 pages, 2007. https://doi.org/10.1155/2007/14567

A Framework to Support Automated Classification and Labeling of Brain Electromagnetic Patterns

Academic Editor: Saied Sanei
Received19 Feb 2007
Revised28 Jul 2007
Accepted07 Oct 2007
Published06 Dec 2007

Abstract

This paper describes a framework for automated classification and labeling of patterns in electroencephalographic (EEG) and magnetoencephalographic (MEG) data. We describe recent progress on four goals: 1) specification of rules and concepts that capture expert knowledge of event-related potentials (ERP) patterns in visual word recognition; 2) implementation of rules in an automated data processing and labeling stream; 3) data mining techniques that lead to refinement of rules; and 4) iterative steps towards system evaluation and optimization. This process combines top-down, or knowledge-driven, methods with bottom-up, or data-driven, methods. As illustrated here, these methods are complementary and can lead to development of tools for pattern classification and labeling that are robust and conceptually transparent to researchers. The present application focuses on patterns in averaged EEG (ERP) data. We also describe efforts to extend our methods to represent patterns in MEG data, as well as EM patterns in source (anatomical) space. The broader aim of this work is to design an ontology-based system to support cross-laboratory, cross-paradigm, and cross-modal integration of brain functional data. Tools developed for this project are implemented in MATLAB and are freely available on request.

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Copyright © 2007 Gwen A. Frishkoff 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.


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