Journal article
Model comparison for automatic characterization and classification of average ERPs using visual oddball paradigm
Clinical neurophysiology, v 120(2), pp 264-274
01 Feb 2009
PMID: 19062338
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Objective: To determine whether automated classifiers can be used for correctly identifying target categorization responses from averaged event-related potentials (ERPs) along with identifying appropriate features and classification models for computer-assisted investigation of attentional processes.
Methods: ERPs were recorded during a target categorization task. Automated classification of average target ERPs versus average non-target ERPs was performed by extracting different combinations of features from the P300 and N200 components, which were used to train six classifiers: Euclidean classifier (EC), Mahalanobis discriminant (MD), quadratic classifier (QC), Fisher linear discriminant (FLD), multi-layer perceptron neural network (MLP) and support vector machine (SVM).
Results: The best classification performance (accuracy: 91-92%; sensitivity: 85-86%; specificity: 95-99%) was provided by QC, MLP, SVM on feature vectors extracted from P300 recorded at multiple sites. In general, non-linear and non-parametric classifiers (QC, MLP, SVM) performed better than linear classifiers (EC, MD, FLD). The N200 did not explain variance beyond that of P300 recorded at multiple sites.
Conclusions: The results suggest that automatic characterization and classification of average target and non-target ERPs is feasible. Features of P300 recorded at multiple sites used to train non-linear classifiers are recommended for optimal classification performance.
Significance: Automatic characterization of target ERPs can provide an objective approach for detecting and diagnosing abnormalities and evaluating interventions for clinical populations, paving the way for future real-time monitoring of attentional processes. (C) 2008 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
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Details
- Title
- Model comparison for automatic characterization and classification of average ERPs using visual oddball paradigm
- Creators
- A. C. Merzagora - Drexel UniversityM. Butti - University of MilanR. Polikar - Rowan UniversityM. Izzetoglu - Drexel UniversityS. Bunce - Drexel UniversityS. Cerutti - University of MilanA. M. Bianchi - University of MilanB. Onaral - Drexel University
- Publication Details
- Clinical neurophysiology, v 120(2), pp 264-274
- Publisher
- Elsevier
- Number of pages
- 11
- Grant note
- N00014-02-1-0524; N00014-01-10986; N00014-04-1-0119 / Office of Naval Research (ONR) and Homeland Security; Office of Naval Research HINT@Lecco Defense Advanced Research Projects Agency (DARPA) Augmented Cognition Program; United States Department of Defense; Defense Advanced Research Projects Agency (DARPA)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000264040100007
- Scopus ID
- 2-s2.0-59149106362
- Other Identifier
- 991019169793704721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Clinical Neurology
- Neurosciences