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

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.

An Analysis of the External Validity of EEG Spectral Power in an Uncontrolled Outdoor Environment during Default and Complex Neurocognitive States

“…Moreover, the resting EEG power spectrum is modulated according to whether an individual maintains the resting state with eyes open and closed. Most studies consistently find greater theta, alpha, and beta spectral power during eyes closed versus eyes open states [ 35 , 36 , 37 , 38 , 41 , 47 , 61 , 62 ], differences that in part reflect a transition from “cortical idling” in the absence of visual or cognitive stimulation to active perceptual and cognitive engagement [ 80 ]. We also observed this eyes closed versus eyes open activity pattern across both environments within all three frequency bands in the present study.…”

“…We chose studies to include in the meta-analysis according to the following criteria: (1) a study utilized a resting state task, an arithmetic task, and/or the PASAT, (2) a study used a repeated-measures contrast to compare EEG power across experimental variables similar to the present study (resting task: eyes open versus eyes closed; PASAT: arithmetic performance versus rest or similar control condition); and (3) a study explicitly reported the information necessary to compute effect size (means, standard deviations/errors, and/or values of inferential statistics). Using these criteria, we identified 7 studies using the resting task, with 4 of these studies measuring theta and beta power [ 37 , 47 , 61 , 62 ], and all 7 studies measuring alpha power [ 35 , 36 , 37 , 38 , 47 , 61 , 62 ]. We also identified 9 studies using an arithmetic task or the PASAT, with 5 of these studies measuring theta power [ 63 , 64 , 65 , 66 , 67 ], 7 measuring alpha power [ 63 , 64 , 65 , 67 , 68 , 69 , 70 ], and 3 studies measuring beta power [ 66 , 69 , 71 ].…”

“…However, in order to assess QEEG metrics reflecting cognitive engagement during the PASAT, a comparison condition is needed in which cognition is relatively disengaged. Here the control condition was EEG activity recorded while participants performed a simple resting state task [ 35 , 36 , 37 , 38 ], where an individual remains in an unstimulated state of wakeful rest in a manner that engages a default mode of brain activity [ 39 ]. This resting task was also of interest in and of itself as a metric of the effects of background environment on the endogenous neurocognitive processing of the default mode state.…”

“…Other studies have used measures of spectral power or functional connectivity to study the technical performance of mobile EEG equipment rather than brain function per se [ 7 , 9 ]. Here, we utilized EEG spectral power as the QEEG metric because previous research has demonstrated correlations between EEG oscillations within three frequency ranges (theta: 4–7 Hz, alpha: 8–13 Hz, low beta: 14–20 Hz) and the neurocognitive states engaged by the PASAT and resting state task [ 35 , 36 , 37 , 38 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ].…”

Time series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattented listening and frequency-modulated tones classification

“…Plenty of them are time-frequency analysis, with the use of tools such as wavelets or matching pursuit [7, 8, 17, 22, 25, 47, 49]. Some of them are fractal or entropy based [45, 51]. Others use the independent component analysis and similar techniques [26, 3, 44, 23].…”