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dc.contributor.authorTrujillo, Logan ( Orcid Icon 0000-0002-1299-0350 )
dc.date.accessioned2021-04-14T17:32:52Z
dc.date.available2021-04-14T17:32:52Z
dc.date.issued2019-01
dc.identifier.citationTrujillo, L. T. (2019). K-th nearest neighbor (KNN) entropy estimates of complexity and integration from ongoing and stimulus-evoked electroencephalographic (EEG) recordings of the human brain. Entropy, 21(1): 61.en_US
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/13378
dc.description.abstractInformation-theoretic measures for quantifying multivariate statistical dependence have proven useful for the study of the unity and diversity of the human brain. Two such measures–integration, I(X), and interaction complexity, CI(X)–have been previously applied to electroencephalographic (EEG) signals recorded during ongoing wakeful brain states. Here, I(X) and CI(X) were computed for empirical and simulated visually-elicited alpha-range (8–13 Hz) EEG signals. Integration and complexity of evoked (stimulus-locked) and induced (non-stimulus-locked) EEG responses were assessed using nonparametric k-th nearest neighbor (KNN) entropy estimation, which is robust to the nonstationarity of stimulus-elicited EEG signals. KNN-based I(X) and CI(X) were also computed for the alpha-range EEG of ongoing wakeful brain states. I(X) and CI(X) patterns differentiated between induced and evoked EEG signals and replicated previous wakeful EEG findings obtained using Gaussian-based entropy estimators. Absolute levels of I(X) and CI(X) were related to absolute levels of alpha-range EEG power and phase synchronization, but stimulus-related changes in the information-theoretic and other EEG properties were independent. These findings support the hypothesis that visual perception and ongoing wakeful mental states emerge from complex, dynamical interaction among segregated and integrated brain networks operating near an optimal balance between order and disorder.en_US
dc.formatText
dc.format.extent34 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.sourceEntropy, 2019, Vol. 21, No. 1, Article 61.
dc.subjectElectroencephalography (EEG)en_US
dc.subjectEEG complexityen_US
dc.subjectEEG integrationen_US
dc.subjectInduced EEGen_US
dc.subjectEvoked EEGen_US
dc.subjectResting state EEGen_US
dc.subjectBrain criticalityen_US
dc.subjectVisual categorizationen_US
dc.titleK-th Nearest Neighbor (KNN) Entropy Estimates of Complexity and Integration from Ongoing and Stimulus-Evoked Electroencephalographic (EEG) Recordings of the Human Brainen_US
dc.typepublishedVersion
txstate.documenttypeArticle
dc.rights.holder© 2019 The Author.
dc.identifier.doihttps://doi.org/10.3390/e21010061
dc.rights.licenseCreative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.description.departmentPsychology


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