DC Field | Value | Language |
---|---|---|
dc.contributor.author | Latchoumane, Charles-Francois Vincent | ko |
dc.contributor.author | Jeong, Jaeseung | ko |
dc.date.accessioned | 2011-01-07T05:18:36Z | - |
dc.date.available | 2011-01-07T05:18:36Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2011-04 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, v.58, no.4, pp.1084 - 1093 | - |
dc.identifier.issn | 0018-9294 | - |
dc.identifier.uri | http://hdl.handle.net/10203/21471 | - |
dc.description.abstract | The brain shows complex, nonstationarity temporal dynamics, with abrupt micro- and macrostate transitions during its information processing. Detecting and characterizing these transitions in dynamical states of the brain is a critical issue in the field of neuroscience and psychiatry. In the current study, a novel method is proposed to quantify brain macrostates (e. g., sleep stages or cognitive states) from shifts of dynamical microstates or dynamical nonstationarity. A "dynamical microstate" is a temporal unit of the information processing in the brain with fixed dynamical parameters and specific spatial distribution. In this proposed approach, a phase-space-based dynamical dissimilarity map (DDM) is used to detect transitions between dynamically stationary microstates in the time series, and Tsallis time-dependent entropy is applied to quantify dynamical patterns of transitions in the DDM. We demonstrate that the DDM successfully detects transitions between microstates of different temporal dynamics in the simulated physiological time series against high levels of noise. Based on the assumption of nonlinear, deterministic brain dynamics, we also demonstrate that dynamical nonstationarity analysis is useful to quantify brain macrostates (sleep stages I, II, III, IV, and rapid eye movement (REM) sleep) from sleep EEGs with an overall accuracy of 77%. We suggest that dynamical nonstationarity is a useful tool to quantify macroscopic mental states (statistical integration) of the brain using dynamical transitions at the microscopic scale in physiological data. | - |
dc.description.sponsorship | The authors would like to thank Drs. Lee M. Hively (Oak Ridge National Laboratory, USA) and Thomas Schreiber (Max Planck Institute for physics of Complex Systems, Germany) for their valuable comments on this method. This work was supported by a Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MOST) (No. R01-2007-000-21094-0 and No. M10644000013-06N4400-01310). The authors thank the Ministry of Information and Technology of South Korea, the Institute for Information and Technology Advancement (IITA) and the Chung MoonSoul Research center at KAIST for their financial support. | en |
dc.language | English | - |
dc.language.iso | en_US | en |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | HUMAN SLEEP EEG | - |
dc.subject | DETRENDED FLUCTUATION ANALYSIS | - |
dc.subject | NONEXTENSIVE STATISTICS | - |
dc.subject | CORRELATION DIMENSION | - |
dc.subject | MICROSTATE DURATION | - |
dc.subject | NONLINEAR STRUCTURE | - |
dc.subject | SCALP EEG | - |
dc.subject | ELECTROENCEPHALOGRAM | - |
dc.subject | CLASSIFICATION | - |
dc.subject | SEGMENTATION | - |
dc.title | Quantification of Brain Macrostates Using Dynamical Nonstationarity of Physiological Time Series | - |
dc.type | Article | - |
dc.identifier.wosid | 000288694300028 | - |
dc.identifier.scopusid | 2-s2.0-79952983540 | - |
dc.type.rims | ART | - |
dc.citation.volume | 58 | - |
dc.citation.issue | 4 | - |
dc.citation.beginningpage | 1084 | - |
dc.citation.endingpage | 1093 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | - |
dc.identifier.doi | 10.1109/TBME.2009.2034840 | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Jeong, Jaeseung | - |
dc.contributor.nonIdAuthor | Latchoumane, Charles-Francois Vincent | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Brain dynamics | - |
dc.subject.keywordAuthor | dissimilarity map | - |
dc.subject.keywordAuthor | dynamical nonstationarity | - |
dc.subject.keywordAuthor | EEG | - |
dc.subject.keywordAuthor | microstates and macrostates | - |
dc.subject.keywordPlus | HUMAN SLEEP EEG | - |
dc.subject.keywordPlus | DETRENDED FLUCTUATION ANALYSIS | - |
dc.subject.keywordPlus | NONEXTENSIVE STATISTICS | - |
dc.subject.keywordPlus | CORRELATION DIMENSION | - |
dc.subject.keywordPlus | MICROSTATE DURATION | - |
dc.subject.keywordPlus | NONLINEAR STRUCTURE | - |
dc.subject.keywordPlus | SCALP EEG | - |
dc.subject.keywordPlus | ELECTROENCEPHALOGRAM | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | SEGMENTATION | - |
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