Special Issue "Entropy Applications in EEG/MEG"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editor

Guest Editor
Prof. Dr. Carlos Gómez Website E-Mail
Biomedical Engineering Group, Universidad de Valladolid, Paseo Belén, 15, 47011 Valladolid, Spain
Interests: biomedical signals; signal processing; nonlinear analyses; connectivity measures; electroencephalography; magnetoencephalography

Special Issue Information

Dear Colleagues,

Despite the combined efforts of scientists around the globe, the human brain remains the great unknown of the human body. Several exploratory techniques have been developed in the last decades to gain insight into its organization and functioning: functional magnetic resonance (fMRI), positron emission tomography (PET), near-infrared spectroscopy (NIRS), electroencephalography (EEG), magnetoencephalography (MEG), and so on. Among these neuroimaging techniques, EEG and MEG are the only signals that record the synchronous oscillations of cortex pyramidal neurons directly and non-invasively.

In many studies, different signal processing methods (spectral measures, nonlinear methods, synchronization estimators, networks parameters, etc.) have been applied to EEG/MEG recordings to extract information about these complex signals. Entropy measurements are being increasingly used for this purpose. Typically, EEG and MEG have been analyzed using spectral entropies and embedding entropies. Spectral entropies (e.g. Shannon entropy, Tsallis entropy, and Renyi entropy) extract information from the amplitude component of the frequency spectrum, whereas embedding entropies (e.g. approximate entropy, sample entropy, and fuzzy entropy) are calculated directly using a time series. Recently, other entropy families have been proposed" instead of "Recently, other spectral families have been proposed. This Special Issue focuses on the application of entropy measurements to analyze and/or characterize the EEG and/or MEG activity at different physiological states and pathological conditions.

Prof. Carlos Gomez
Guest Editor

Manuscript Submission Information

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Keywords

  • Applications of spectral entropies in EEG and MEG
  • Applications of embedding entropies in EEG and MEG
  • Applications of multiscale entropies in EEG and MEG
  • Applications of cross-entropy measures in EEG and MEG
  • Entropy and brain–computer interface applications
  • Entropy and brain disorders
  • New entropy measures. EEG and MEG applications

Published Papers (3 papers)

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Research

Open AccessArticle
Interpretation of Entropy Algorithms in the Context of Biomedical Signal Analysis and Their Application to EEG Analysis in Epilepsy
Entropy 2019, 21(9), 840; https://doi.org/10.3390/e21090840 - 27 Aug 2019
Abstract
Biomedical signals are measurable time series that describe a physiological state of a biological system. Entropy algorithms have been previously used to quantify the complexity of biomedical signals, but there is a need to understand the relationship of entropy to signal processing concepts. [...] Read more.
Biomedical signals are measurable time series that describe a physiological state of a biological system. Entropy algorithms have been previously used to quantify the complexity of biomedical signals, but there is a need to understand the relationship of entropy to signal processing concepts. In this study, ten synthetic signals that represent widely encountered signal structures in the field of signal processing were created to interpret permutation, modified permutation, sample, quadratic sample and fuzzy entropies. Subsequently, the entropy algorithms were applied to two different databases containing electroencephalogram (EEG) signals from epilepsy studies. Transitions from randomness to periodicity were successfully detected in the synthetic signals, while significant differences in EEG signals were observed based on different regions and states of the brain. In addition, using results from one entropy algorithm as features and the k-nearest neighbours algorithm, maximum classification accuracies in the first EEG database ranged from 63% to 73.5%, while these values increased by approximately 20% when using two different entropies as features. For the second database, maximum classification accuracy reached 62.5% using one entropy algorithm, while using two algorithms as features further increased that by 10%. Embedding entropies (sample, quadratic sample and fuzzy entropies) are found to outperform the rest of the algorithms in terms of sensitivity and show greater potential by considering the fine-tuning possibilities they offer. On the other hand, permutation and modified permutation entropies are more consistent across different input parameter values and considerably faster to calculate. Full article
(This article belongs to the Special Issue Entropy Applications in EEG/MEG)
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Open AccessFeature PaperArticle
Sex Differences in the Complexity of Healthy Older Adults’ Magnetoencephalograms
Entropy 2019, 21(8), 798; https://doi.org/10.3390/e21080798 - 15 Aug 2019
Abstract
The analysis of resting-state brain activity recording in magnetoencephalograms (MEGs) with new algorithms of symbolic dynamics analysis could help obtain a deeper insight into the functioning of the brain and identify potential differences between males and females. Permutation Lempel-Ziv complexity (PLZC), a recently [...] Read more.
The analysis of resting-state brain activity recording in magnetoencephalograms (MEGs) with new algorithms of symbolic dynamics analysis could help obtain a deeper insight into the functioning of the brain and identify potential differences between males and females. Permutation Lempel-Ziv complexity (PLZC), a recently introduced non-linear signal processing algorithm based on symbolic dynamics, was used to evaluate the complexity of MEG signals in source space. PLZC was estimated in a broad band of frequencies (2–45 Hz), as well as in narrow bands (i.e., theta (4–8 Hz), alpha (8–12 Hz), low beta (12–20 Hz), high beta (20–30 Hz), and gamma (30–45 Hz)) in a sample of 98 healthy elderly subjects (49 males, 49 female) aged 65–80 (average age of 72.71 ± 4.22 for males and 72.67 ± 4.21 for females). PLZC was significantly higher for females than males in the high beta band at posterior brain regions including the precuneus, and the parietal and occipital cortices. Further statistical analyses showed that higher complexity values over highly overlapping regions than the ones mentioned above were associated with larger hippocampal volumes only in females. These results suggest that sex differences in healthy aging can be identified from the analysis of magnetoencephalograms with novel signal processing methods. Full article
(This article belongs to the Special Issue Entropy Applications in EEG/MEG)
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Open AccessFeature PaperArticle
EEG Characterization of the Alzheimer’s Disease Continuum by Means of Multiscale Entropies
Entropy 2019, 21(6), 544; https://doi.org/10.3390/e21060544 - 28 May 2019
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder with high prevalence, known for its highly disabling symptoms. The aim of this study was to characterize the alterations in the irregularity and the complexity of the brain activity along the AD continuum. Both irregularity and [...] Read more.
Alzheimer’s disease (AD) is a neurodegenerative disorder with high prevalence, known for its highly disabling symptoms. The aim of this study was to characterize the alterations in the irregularity and the complexity of the brain activity along the AD continuum. Both irregularity and complexity can be studied applying entropy-based measures throughout multiple temporal scales. In this regard, multiscale sample entropy (MSE) and refined multiscale spectral entropy (rMSSE) were calculated from electroencephalographic (EEG) data. Five minutes of resting-state EEG activity were recorded from 51 healthy controls, 51 mild cognitive impaired (MCI) subjects, 51 mild AD patients (ADMIL), 50 moderate AD patients (ADMOD), and 50 severe AD patients (ADSEV). Our results show statistically significant differences (p-values < 0.05, FDR-corrected Kruskal–Wallis test) between the five groups at each temporal scale. Additionally, average slope values and areas under MSE and rMSSE curves revealed significant changes in complexity mainly for controls vs. MCI, MCI vs. ADMIL and ADMOD vs. ADSEV comparisons (p-values < 0.05, FDR-corrected Mann–Whitney U-test). These findings indicate that MSE and rMSSE reflect the neuronal disturbances associated with the development of dementia, and may contribute to the development of new tools to track the AD progression. Full article
(This article belongs to the Special Issue Entropy Applications in EEG/MEG)
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