@article{3dda44f278c34f10a6ea915ca077bead,
title = "A framework to support automated classification and labeling of brain electromagnetic patterns",
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.",
author = "Frishkoff, {Gwen A.} and Frank, {Robert M.} and Jiawei Rong and Dejing Dou and Joseph Dien and Halderman, {Laura K.}",
note = "Funding Information: Frishkoff Gwen A. gwenf@pitt.edu 1 Frank Robert M. rmfrank@mac.com 2 Rong Jiawei jrong@cs.uoregon.edu 3 Dou Dejing dou@cs.uoregon.edu 3 Dien Joseph jdien@ku.edu 4 Halderman Laura K. lkh11@pitt.edu 1 Sanei Saied 1 Learning Research and Development Center University of Pittsburgh Pittsburgh, PA 15260 USA pitt.edu 2 NeuroInformatics Center University of Oregon 1600 Millrace Drive Eugene, OR 97403 USA uoregon.edu 3 Computer and Information Sciences University of Oregon Eugene, OR 97403 USA uoregon.edu 4 Department of Psychology University of Kansas 1415 Jayhawk Boulevard Lawrence, KS 66045 USA ku.edu 2007 06 12 2007 2007 19 02 2007 28 07 2007 07 10 2007 2007 Copyright {\textcopyright} 2007 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. 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. Telemedicine Advanced Technology Research Center DAMD17-01-1-0750 http://dx.doi.org/10.13039/100000001 National Science Foundation BCS-0321388 ",
year = "2007",
doi = "10.1155/2007/14567",
language = "English (US)",
volume = "2007",
journal = "Computational intelligence and neuroscience",
issn = "1687-5265",
publisher = "Hindawi Publishing Corporation",
}