About this Journal Submit a Manuscript Table of Contents
Computational Intelligence and Neuroscience
Volume 2007 (2007), Article ID 14567, 13 pages
http://dx.doi.org/10.1155/2007/14567
Research Article

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

1Learning Research and Development Center, University of Pittsburgh, Pittsburgh 15260, PA, USA
2NeuroInformatics Center, University of Oregon, 1600 Millrace Drive, Eugene 97403, OR, USA
3Computer and Information Sciences, University of Oregon, Eugene 97403, OR, USA
4Department of Psychology, University of Kansas, 1415 Jayhawk Boulevard, Lawrence 66045, KS, USA

Received 19 February 2007; Revised 28 July 2007; Accepted 7 October 2007

Academic Editor: Saied Sanei

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.

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.