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Conference Paper: Topographical segmentation: A new tool to optimally define temporal region-of-interests of significant difference in ERPs

TitleTopographical segmentation: A new tool to optimally define temporal region-of-interests of significant difference in ERPs
Authors
Issue Date2014
PublisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001228
Citation
The 19th International Conference on Digital Signal Processing (DSP), Hong Kong, China, 20-23 August 2014. In Proceedings of the International Conference on Digital Signal Processing, 2014, p. 789-792 How to Cite?
AbstractThe statistical identification of temporal region-of-interests (ROIs) of the significant difference in event-related potentials (ERPs) was popularly achieved using the cluster-based approach, in which the clustering was achieved based on the temporal adjacency of statistical significance if data from single-electrode were tested, or based on the spatial and temporal adjacency of statistical significance if data from multi-electrodes were tested. However, this cluster-based approach would be problematic if the significant differences were strong and sustained in time, but varied greatly in space. In other words, neural generators, which contributed to the detected significant differences, changed markedly within the explored temporal-cluster. To solve this problem, we implemented a statistical approach based on topographical segmentation analysis, which did not only make use of the temporal adjacency of significance, but also utilized the scalp distribution of statistical difference. We applied this technique to assess the significant difference of SEPs between deviant and standard conditions, and we observed that temporal ROIs, captured distinct spatial distributions of statistical difference, could be correctly identified using the topographical segmentation analysis be means of quasi-stable scalp distribution.
Persistent Identifierhttp://hdl.handle.net/10722/204090

 

DC FieldValueLanguage
dc.contributor.authorHu, Len_US
dc.contributor.authorShen, JSen_US
dc.contributor.authorZhang, Zen_US
dc.date.accessioned2014-09-19T20:05:05Z-
dc.date.available2014-09-19T20:05:05Z-
dc.date.issued2014en_US
dc.identifier.citationThe 19th International Conference on Digital Signal Processing (DSP), Hong Kong, China, 20-23 August 2014. In Proceedings of the International Conference on Digital Signal Processing, 2014, p. 789-792en_US
dc.identifier.urihttp://hdl.handle.net/10722/204090-
dc.description.abstractThe statistical identification of temporal region-of-interests (ROIs) of the significant difference in event-related potentials (ERPs) was popularly achieved using the cluster-based approach, in which the clustering was achieved based on the temporal adjacency of statistical significance if data from single-electrode were tested, or based on the spatial and temporal adjacency of statistical significance if data from multi-electrodes were tested. However, this cluster-based approach would be problematic if the significant differences were strong and sustained in time, but varied greatly in space. In other words, neural generators, which contributed to the detected significant differences, changed markedly within the explored temporal-cluster. To solve this problem, we implemented a statistical approach based on topographical segmentation analysis, which did not only make use of the temporal adjacency of significance, but also utilized the scalp distribution of statistical difference. We applied this technique to assess the significant difference of SEPs between deviant and standard conditions, and we observed that temporal ROIs, captured distinct spatial distributions of statistical difference, could be correctly identified using the topographical segmentation analysis be means of quasi-stable scalp distribution.-
dc.languageengen_US
dc.publisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001228-
dc.relation.ispartofProceedings of the International Conference on Digital Signal Processingen_US
dc.rightsProceedings of the International Conference on Digital Signal Processing. Copyright © I E E E.-
dc.rights©2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleTopographical segmentation: A new tool to optimally define temporal region-of-interests of significant difference in ERPsen_US
dc.typeConference_Paperen_US
dc.identifier.emailZhang, Z: zgzhang@eee.hku.hken_US
dc.identifier.authorityZhang, Z=rp01565en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDSP.2014.6900772-
dc.identifier.hkuros238878en_US
dc.identifier.spage789-
dc.identifier.epage792-
dc.publisher.placeUnited State-

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