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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
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.
- W. Klimesch, “Memory processes, brain oscillations and EEG synchronization,” International Journal of Psychophysiology, vol. 24, no. 1-2, pp. 61–100, 1996.
- T.-P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne, and T. J. Sejnowski, “Analysis and visualization of single-trial event-related potentials,” Human Brain Mapping, vol. 14, no. 3, pp. 166–185, 2001.
- M. Fabiani, G. Gratton, and M. G. H. Coles, “Event-related brain poten-tials: methods, theory, and applications,” in Handbook of Psychophysiology, J. Cacioppo, L. Tassinary, and G. Berntson, Eds., pp. 53–84, Cambridge University Press, New York, NY, USA, 2000, chapter 3.
- A. M. Proverbio and A. Zani, “Electromagnetic manifestations of mind and brain,” in The Cognitive Electrophysiology of Mind and Brain, A. Zani and A. M. Proverbio, Eds., pp. 13–37, Academic Press, New York, NY, USA, 2002, chapter 2.
- T. Gasser, J. C. Schuller, and U. S. Gasser, “Correction of muscle artefacts in the EEG power spectrum,” Clinical Neurophysiology, vol. 116, no. 9, pp. 2044–2050, 2005.
- O. Hauk, M. H. Davis, M. Ford, F. Pulvermüller, and W. D. Marslen-Wilson, “The time course of visual word recognition as revealed by linear regression analysis of ERP data,” NeuroImage, vol. 30, no. 4, pp. 1383–1400, 2006.
- K. Spencer, “Averaging, detection, and classification of single-trials ERPs,” in Event-Related Potentials: A Methods Handbook, T. Handy, Ed., pp. 209–228, MIT Press, Cambridge, Mass, USA, 2005.
- E. Donchin and E. Heffley, “Multivariate analysis of event-related potential data: a tutorial review,” in Multidisciplinary Perspectives in Event-Related Brain Potential Research, D. Otto, Ed., pp. 555–572, U.S. Government Printing Office, Washington, DC, USA, 1978.
- T. W. Picton, S. Bentin, P. Berg et al., “Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria,” Psychophysiology, vol. 37, no. 2, pp. 127–152, 2000.
- P. L. Nunez, Electric Fields of the Brain: The Neurophysics of EEG, Oxford University Press, New York, NY, USA, 1981.
- G. Gratton, M. G. H. Coles, and E. Donchin, “A procedure for using multi-electrode information in the analysis of components of the event-related potential: Vector filter,” Psychophysiology, vol. 26, no. 2, pp. 222–232, 1989.
- K. M. Spencer, J. Dien, and E. Donchin, “A componential analysis of the ERP elicited by novel events using a dense electrode array,” Psychophysiology, vol. 36, no. 3, pp. 409–414, 1999.
- T. Handy, “Basic principles of ERP quantification,” in Event-Related Potentials: A Methods Handbook, T. Handy, Ed., pp. 33–56, MIT Press, Cambridge, Mass, USA, 2005.
- A. C. Nobre and G. McCarthy, “Language-related ERPs: scalp distributions and modulation by word type and semantic priming,” Journal of Cognitive Neuroscience, vol. 6, no. 3, pp. 233–255, 1994.
- J. Dien, G. A. Frishkoff, A. Cerbone, and D. M. Tucker, “Parametric analysis of event-related potentials in semantic comprehension: evidence for parallel brain mechanisms,” Cognitive Brain Research, vol. 15, no. 2, pp. 137–153, 2003.
- G. A. Frishkoff, “Hemispheric differences in strong versus weak semantic priming: evidence from event-related brain potentials,” Brain and Language, vol. 100, no. 1, pp. 23–43, 2007.
- P. E. Compton, P. Grossenbacher, M. I. Posner, and D. M. Tucker, “A cognitive-anatomical approach to attention in lexical access,” Journal of Cognitive Neuroscience, vol. 3, no. 4, pp. 304–312, 1991.
- K. Huang, K. Itoh, S. Suwazono, and T. Nakada, “Electrophysiological correlates of grapheme-phoneme conversion,” Neuroscience Letters, vol. 366, no. 3, pp. 254–258, 2004.
- J. Dien and G. A. Frishkoff, “Introduction to principal components analysis of event-related potentials,” in Event-Related Potentials: A Methods Handbook, T. Handy, Ed., pp. 189–208, MIT Press, Cambridge, Mass, USA, 2005.
- J. Dien, “PCA toolbox (version 1.093),” October 2004, Lawrence, Kan, USA.
- J. Dien, “Addressing misallocation of variance in principal components analysis of event-related potentials,” Brain Topography, vol. 11, no. 1, pp. 43–55, 1998.
- D. Dou, G. Frishkoff, J. Rong, R. M. Frank, A. Malony, and D. M. Tucker, “Development of NeuroElectroMagnetic Ontologies (NEMO): a framework for mining brainwave ontologies,” in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '07), pp. 270–279, San Jose, Calif, USA, August 2007.
- J. Rong, D. Dou, G. A. Frishkoff, R. M. Frank, A. Malony, and D. M. Tucker, “A semi-automatic framework for mining ERP patterns,” in Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW '07), vol. 1, pp. 329–334, Niagara Falls, Canada, May 2007.
- A. Rodriguez-Fornells, B. M. Schmitt, M. Kutas, and T. F. Münte, “Electrophysiological estimates of the time course of semantic and phonological encoding during listening and naming,” Neuropsychologia, vol. 40, no. 7, pp. 778–787, 2002.
- A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” Journal of the Royal Statistical Society Series B, vol. 39, no. 1, pp. 1–38, 1977.
- Weka 3, “Data Mining Software in Java,” http://www.cs.waikato.ac.nz/ml/weka/.
- J. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, Calif, USA, 1993.
- G. A. Frishkoff, C. Perfetti, and C. Westbury, “ERP measures of partial semantics knowledge: left temporal indices of skill differences and lexical quality,” Biological Psychology, in revision.
- D. A. Medler and J. R. Binder, “MCWord: an online orthographic database of the English language,” 2005, http://www.neuro.mcw.edu/mcword/.
- M. D. Wilson, “The MRC psycholin-guistic database: machine readable dictionary,” Behavioural Research Methods, Instruments and Computers, vol. 20, no. 1, pp. 6–11, 1988.
- Electrical Geodesics (EGI), “Eugene, Oregon,” http://www.egi.com/.
- J. Dien, “Issues in the application of the average reference: review, critiques, and recommendations,” Behavior Research Methods, Instruments, and Computers, vol. 30, no. 1, pp. 34–43, 1998.
- M. Junghöfer, T. Elbert, D. M. Tucker, and C. Braun, “The polar average reference effect: a bias in estimating the head surface integral in EEG recording,” Clinical Neurophysiology, vol. 110, no. 6, pp. 1149–1155, 1999.
- M. Junghöfer, T. Elbert, D. M. Tucker, and B. Rockstroh, “Statistical control of artifacts in dense array EEG/MEG studies,” Psychophysiology, vol. 37, no. 4, pp. 523–532, 2000.
- G. McCarthy and C. C. Wood, “Scalp distributions of event-related potentials: an ambiguity associated with analysis of variance models,” Electroencephalography and Clinical Neurophysiology, vol. 62, no. 3, pp. 203–208, 1985.
- R. Rosenthal and R. Rosnow, Essentials of Behavioral Research: Methods and Data Analysis, McGraw-Hill, New York, NY, USA, 2nd edition, 1991.
- D. Lehmann and W. Skrandies, “Spatial analysis of evoked potentials in man—a review,” Progress in Neurobiology, vol. 23, no. 3, pp. 227–250, 1984.
- “Cartool software,” Functional Brain Mapping Laboratory, Geneva, Switzerland, http://brainmapping.unige.ch/Cartool.htm.
- T. Koenig, K. Kochi, and D. Lehmann, “Event-related electric microstates of the brain differ between words with visual and abstract meaning,” Electroencephalography and Clinical Neurophysiology, vol. 106, no. 6, pp. 535–546, 1998.
- T. Koenig and D. Lehmann, “Microstates in language-related brain potential maps show noun-verb differences,” Brain and Language, vol. 53, no. 2, pp. 169–182, 1996.
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