K-th Nearest Neighbor (KNN) Entropy Estimates of Complexity and Integration from Ongoing and Stimulus-Evoked Electroencephalographic (EEG) Recordings of the Human Brain
Date
2019-01
Authors
Trujillo, Logan
Journal Title
Journal ISSN
Volume Title
Publisher
Multidisciplinary Digital Publishing Institute
Abstract
Information-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.
Description
Keywords
electroencephalography (EEG), EEG complexity, EEG integration, induced EEG, evoked EEG, resting state EEG, brain criticality, visual categorization, Psychology
Citation
Trujillo, 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.
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© 2019 The Author.
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This work is licensed under a Creative Commons Attribution 4.0 International License.