K-th Nearest Neighbor (KNN) Entropy Estimates of Complexity and Integration from Ongoing and Stimulus-Evoked Electroencephalographic (EEG) Recordings of the Human Brain

K-th Nearest Neighbor (KNN) Entropy Estimates of Complexity and Integration from Ongoing and Stimulus-Evoked Electroencephalographic (EEG) Recordings of the Human Brain

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 electroencepha...

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Bibliographic Details
Main Author: Logan T. Trujillo
Format: Article
Language:English
Published: MDPI AG 2019-01-01
Series:Entropy
Subjects:
Electroencephalography (EEG)
EEG complexity
EEG integration
induced EEG
evoked EEG
resting state EEG
brain criticality
visual categorization
Online Access:http://www.mdpi.com/1099-4300/21/1/61

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http://www.mdpi.com/1099-4300/21/1/61

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