Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/71123
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dc.titleNeuronal optical response recognition based on chaos levels identification of near-infrared spectroscopy time series
dc.contributor.authorHu, X.-S.
dc.contributor.authorHong, K.-S.
dc.contributor.authorGe, S.S.
dc.date.accessioned2014-06-19T03:20:06Z
dc.date.available2014-06-19T03:20:06Z
dc.date.issued2011
dc.identifier.citationHu, X.-S.,Hong, K.-S.,Ge, S.S. (2011). Neuronal optical response recognition based on chaos levels identification of near-infrared spectroscopy time series. Proceedings of the 30th Chinese Control Conference, CCC 2011 : 3218-3220. ScholarBank@NUS Repository.
dc.identifier.isbn9789881725592
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71123
dc.description.abstractNear-infrared spectroscopy (NIRS) can detect two different kinds of signals from the human brain: the hemodynamic (slow) optical response, and the neuronal (fast) optical response. This paper explores the nonlinear aspects of the tactile-stimulus-evoked neuronal optical response (fast optical response) over a NIRS time series (light intensity variation). The fast optical responses (FORs) over time series recorded in stimulus sessions are confirmed by event-related averaging. The chaos levels of NIRS time series recorded both in stimulus and in rest sessions are then identified according to the estimated largest Lyapunov exponent. The obtained results strongly suggest that the chaos level can be used to recognize the FORs in NIRS time series and, thereby, the state of the pertinent brain activity. © 2011 Chinese Assoc of Automati.
dc.sourceScopus
dc.subjectBrain Signal
dc.subjectChaotic Characteristics
dc.subjectFast Neuronal Response
dc.subjectNear-Infrared Spectroscopy (Nirs)
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.sourcetitleProceedings of the 30th Chinese Control Conference, CCC 2011
dc.description.page3218-3220
dc.identifier.isiutNOT_IN_WOS
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