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Entropy Applications in EEG/MEG

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 27691

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Prof. Dr. Carlos Gómez

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Biomedical Engineering Group, Universidad de Valladolid, Paseo Belén, 15, 47011 Valladolid, Spain
Interests: biomedical signals; signal processing; nonlinear analyses; connectivity measures; electroencephalography; magnetoencephalography
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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

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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 (5 papers)

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Research

23 pages, 7091 KiB

Open AccessArticle

Multiscale Entropy as a New Feature for EEG and fNIRS Analysis

by Thanate Angsuwatanakul, Jamie O’Reilly, Kajornvut Ounjai, Boonserm Kaewkamnerdpong and Keiji Iramina

Entropy 2020, 22(2), 189; https://doi.org/10.3390/e22020189 - 07 Feb 2020

Cited by 25 | Viewed by 5107

Abstract

The present study aims to apply multiscale entropy (MSE) to analyse brain activity in terms of brain complexity levels and to use simultaneous electroencephalogram and functional near-infrared spectroscopy (EEG/fNIRS) recordings for brain functional analysis. A memory task was selected to demonstrate the potential of this multimodality approach since memory is a highly complex neurocognitive process, and the mechanisms governing selective retention of memories are not fully understood by other approaches. In this study, 15 healthy participants with normal colour vision participated in the visual memory task, which involved the making the executive decision of remembering or forgetting the visual stimuli based on his/her own will. In a continuous stimulus set, 250 indoor/outdoor scenes were presented at random, between periods of fixation on a black background. The participants were instructed to make a binary choice indicating whether they wished to remember or forget the image; both stimulus and response times were stored for analysis. The participants then performed a scene recognition test to confirm whether or not they remembered the images. The results revealed that the participants intentionally memorising a visual scene demonstrate significantly greater brain complexity levels in the prefrontal and frontal lobe than when purposefully forgetting a scene; p < 0.05 (two-tailed). This suggests that simultaneous EEG and fNIRS can be used for brain functional analysis, and MSE might be the potential indicator for this multimodality approach. Full article

(This article belongs to the Special Issue Entropy Applications in EEG/MEG)

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25 pages, 3117 KiB

Open AccessArticle

Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting

by Jiang Wu, Tengfei Zhou and Taiyong Li

Entropy 2020, 22(2), 140; https://doi.org/10.3390/e22020140 - 24 Jan 2020

Cited by 68 | Viewed by 8065

Abstract

Epilepsy is a common nervous system disease that is characterized by recurrent seizures. An electroencephalogram (EEG) records neural activity, and it is commonly used for the diagnosis of epilepsy. To achieve accurate detection of epileptic seizures, an automatic detection approach of epileptic seizures, integrating complementary ensemble empirical mode decomposition (CEEMD) and extreme gradient boosting (XGBoost), named CEEMD-XGBoost, is proposed. Firstly, the decomposition method, CEEMD, which is capable of effectively reducing the influence of mode mixing and end effects, was utilized to divide raw EEG signals into a set of intrinsic mode functions (IMFs) and residues. Secondly, the multi-domain features were extracted from raw signals and the decomposed components, and they were further selected according to the importance scores of the extracted features. Finally, XGBoost was applied to develop the epileptic seizure detection model. Experiments were conducted on two benchmark epilepsy EEG datasets, named the Bonn dataset and the CHB-MIT (Children’s Hospital Boston and Massachusetts Institute of Technology) dataset, to evaluate the performance of our proposed CEEMD-XGBoost. The extensive experimental results indicated that, compared with some previous EEG classification models, CEEMD-XGBoost can significantly enhance the detection performance of epileptic seizures in terms of sensitivity, specificity, and accuracy. Full article

(This article belongs to the Special Issue Entropy Applications in EEG/MEG)

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30 pages, 12317 KiB

Open AccessArticle

Interpretation of Entropy Algorithms in the Context of Biomedical Signal Analysis and Their Application to EEG Analysis in Epilepsy

by Lampros Chrysovalantis Amarantidis and Daniel Abásolo

Entropy 2019, 21(9), 840; https://doi.org/10.3390/e21090840 - 27 Aug 2019

Cited by 26 | Viewed by 4707

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. 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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12 pages, 1485 KiB

Open AccessFeature PaperArticle

Sex Differences in the Complexity of Healthy Older Adults’ Magnetoencephalograms

by Elizabeth Shumbayawonda, Daniel Abásolo, David López-Sanz, Ricardo Bruña, Fernando Maestu and Alberto Fernández

Entropy 2019, 21(8), 798; https://doi.org/10.3390/e21080798 - 15 Aug 2019

Cited by 5 | Viewed by 3788

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 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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15 pages, 2696 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

EEG Characterization of the Alzheimer’s Disease Continuum by Means of Multiscale Entropies

by Aarón Maturana-Candelas, Carlos Gómez, Jesús Poza, Nadia Pinto and Roberto Hornero

Entropy 2019, 21(6), 544; https://doi.org/10.3390/e21060544 - 28 May 2019

Cited by 39 | Viewed by 5145

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 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 (AD_MIL), 50 moderate AD patients (AD_MOD), and 50 severe AD patients (AD_SEV). 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. AD_MIL and AD_MOD vs. AD_SEV 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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