Quantification of EEG irregularity by use of the entropy of the power spectrum

Inouye, Tetsuzo; Shinosaki, Kazuhiro; Sakamoto, H.; Toi, Seigo; Ukai, Shigeo; Iyama, Akinori; Katsuda, Y.; Hirano, Makoto

doi:10.1016/0013-4694(91)90138-t

Cited by 340 publications

(238 citation statements)

References 13 publications

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“…Entropy alone can form a useful measure of the complexity of a signal [Abásolo et al, 2006; Inouye et al, 1991; Overath et al, 2007]. However, here we are interested primarily in its relation to MI, which quantifies the relationship between two variables (for example an external stimulus and a recorded signal) in terms of differences in entropies.…”

Section: Review Of Information Theory For Neuroimagingmentioning

confidence: 99%

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Ince

Giordano

Kayser

et al. 2016

Human Brain Mapping

282

298

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We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc.

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Section: Review Of Information Theory For Neuroimagingmentioning

confidence: 99%

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Ince

Giordano

Kayser

et al. 2016

Human Brain Mapping

282

298

View full text Add to dashboard Cite

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“…PSDs were averaged for each channel and subject to compute the mean power spectrum. Then, the RSP was calculated in the classical frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta1 (13)(14)(15)(16)(17)(18)(19), beta2 (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30-64 Hz). If we denote the power at each frequency band as P j , j = {delta, theta, alpha, beta1, beta2, gamma}, then, the RSP can be represented as a probability distribution,…”

Section: B Definition Of the Spectral Entropiesmentioning

confidence: 99%

“…The SSE is a disorder quantifier, which has been previously employed to characterize MEG irregularity in AD [10]. It is a measure of the flatness of the spectrum, and its definition is based on the Shannon's entropy [22],…”

Section: B Definition Of the Spectral Entropiesmentioning

confidence: 99%

Analysis of Spontaneous MEG Activity in Patients with Alzheimer's Disease using Spectral Entropies

Poza

Hornero

Abásolo

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-The aim of this study was to explore the ability of several spectral entropies to discriminate between spontaneous magnetoencephalographic (MEG) oscillations from 20 Alzheimer's disease (AD) patients and 21 controls. Hence, the relative spectral power (RSP) in classical frequency bands was calculated from the averaged power spectral density. Given the fact that the RSP can be viewed as a probability distribution function, the Shannon spectral entropy, Tsallis spectral entropy, generalized escort-Tsallis spectral entropy and Rényi spectral entropy were calculated from the RSP. Significant differences for each parameter were assessed with MannWhitney U test, whereas classification performance was studied using binary logistic regression. Results revealed an increase in the RSP of control subjects at beta and gamma bands, while AD patients showed an increase in the RSP values at delta and theta bands. Entropic quantifiers obtained statistically significant lower values for AD patients than for controls. This issue suggests a significant decrease in irregularity of AD patients' MEG activity.

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“…Spectral entropy was also used for brain electrical signals by Inouye in [5,6]. The Fourier based entropy measures may lead to misleading results when applied to nonstationary signals.…”

Section: Introductionmentioning

confidence: 99%

Continuous time wavelet entropy of auditory evoked potentials

Cek

Özgören

Savacı

2010

Computers in Biology and Medicine

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a b s t r a c tIn this paper, the continuous time wavelet entropy (CTWE) of auditory evoked potentials (AEP) has been characterized by evaluating the relative wavelet energies (RWE) in specified EEG frequency bands. Thus, the rapid variations of CTWE due to the auditory stimulation could be detected in post-stimulus time interval. This approach removes the probability of missing the information hidden in short time intervals. The discrete time and continuous time wavelet based wavelet entropy variations were compared on non-target and target AEP data. It was observed that CTWE can also be an alternative method to analyze entropy as a function of time.

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Quantification of EEG irregularity by use of the entropy of the power spectrum

Cited by 340 publications

References 13 publications

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Analysis of Spontaneous MEG Activity in Patients with Alzheimer's Disease using Spectral Entropies

Continuous time wavelet entropy of auditory evoked potentials

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