A Framework to Support Automated Classification and Labeling of Brain Electromagnetic Patterns
Gwen A. Frishkoff
Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA 15260, USA pitt.edu
Search for more papers by this authorRobert M. Frank
NeuroInformatics Center, University of Oregon, 1600 Millrace Drive, Eugene, OR 97403, USA uoregon.edu
Search for more papers by this authorJiawei Rong
Computer and Information Sciences, University of Oregon, Eugene, OR 97403, USA uoregon.edu
Search for more papers by this authorDejing Dou
Computer and Information Sciences, University of Oregon, Eugene, OR 97403, USA uoregon.edu
Search for more papers by this authorJoseph Dien
Department of Psychology, University of Kansas, 1415 Jayhawk Boulevard, Lawrence, KS 66045, USA ku.edu
Search for more papers by this authorLaura K. Halderman
Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA 15260, USA pitt.edu
Search for more papers by this authorGwen A. Frishkoff
Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA 15260, USA pitt.edu
Search for more papers by this authorRobert M. Frank
NeuroInformatics Center, University of Oregon, 1600 Millrace Drive, Eugene, OR 97403, USA uoregon.edu
Search for more papers by this authorJiawei Rong
Computer and Information Sciences, University of Oregon, Eugene, OR 97403, USA uoregon.edu
Search for more papers by this authorDejing Dou
Computer and Information Sciences, University of Oregon, Eugene, OR 97403, USA uoregon.edu
Search for more papers by this authorJoseph Dien
Department of Psychology, University of Kansas, 1415 Jayhawk Boulevard, Lawrence, KS 66045, USA ku.edu
Search for more papers by this authorLaura K. Halderman
Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA 15260, USA pitt.edu
Search for more papers by this authorAbstract
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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