scholarly journals Entropy as Measure of Brain Networks’ Complexity in Eyes Open and Closed Conditions

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2178
Author(s):  
Fabrizio Vecchio ◽  
Francesca Miraglia ◽  
Chiara Pappalettera ◽  
Alessandro Orticoni ◽  
Francesca Alù ◽  
...  
Keyword(s):  
Chaotic Behavior ◽  
Brain Networks ◽  
Approximate Entropy ◽  
Brain State ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open ◽  
Brain States ◽  
System Disorder ◽  
Electroencephalogram Eeg

Brain complexity can be revealed even through a comparison between two trivial conditions, such as eyes open and eyes closed (EO and EC respectively) during resting. Electroencephalogram (EEG) has been widely used to investigate brain networks, and several non-linear approaches have been applied to investigate EO and EC signals modulation, both symmetric and not. Entropy is one of the approaches used to evaluate the system disorder. This study explores the differences in the EO and EC awake brain dynamics by measuring entropy. In particular, an approximate entropy (ApEn) was measured, focusing on the specific cerebral areas (frontal, central, parietal, occipital, temporal) on EEG data of 37 adult healthy subjects while resting. Each participant was submitted to an EO and an EC resting EEG recording in two separate sessions. The results showed that in the EO condition the cerebral networks of the subjects are characterized by higher values of entropy than in the EC condition. All the cerebral regions are subjected to this chaotic behavior, symmetrically in both hemispheres, proving the complexity of networks dynamics dependence from the subject brain state. Remarkable dynamics regarding cerebral networks during simple resting and awake brain states are shown by entropy. The application of this parameter can be also extended to neurological conditions, to establish and monitor personalized rehabilitation treatments.

Cultivating Chan with Calibration

2015 ◽  
Vol 4 (4) ◽  
pp. 32-51
Author(s):  
Yuezhe Li ◽  
Yuchou Chang ◽  
Hong Lin
Keyword(s):  
Modern Society ◽  
Brain State ◽  
Mental Training ◽  
Eastern Asia ◽  
Machine Learning Methods ◽  
Training Methodology ◽  
Eeg Data ◽  
Brain States ◽  
State Models ◽  
Electroencephalogram Eeg

Chan is a superior mental training methodology derived from Buddhism and absorbed the wisdom of religious practitioners, philosophers, and scholars around Eastern Asia through thousands of years. As the primary way of Chan, meditation has clear effects in bringing practitioners' mind into a tranquil state and promoting both the mental and the physical health. The effect of Chan is measurable. The authors propose to establish a Chan science by applying modern experimental sciences to various models. In particular, machine learning methods have been used to classify brain states using electroencephalogram (EEG) data. The experimental results show potential in building brain state models for calibrating the routine of meditation. Through these studies, the authors believe they will be able to make Chan a beneficial practice to promote human's life in modern society.

CAN WE MEASURE CONSCIOUSNESS WITH EEG COMPLEXITIES?

2003 ◽  
Vol 13 (03) ◽  
pp. 733-742 ◽  
Author(s):  
FANJI GU ◽  
XIN MENG ◽  
ENHUA SHEN ◽  
ZHIJIE CAI
Keyword(s):  
Mutual Information ◽  
Epileptic Seizures ◽  
Approximate Entropy ◽  
Deep Sleep ◽  
Complexity Measures ◽  
Eyes Closed ◽  
Significant Difference ◽  
Eyes Open ◽  
Brain States ◽  
Light Sleep

Several complexity measures, especially approximate entropy (ApEn) and a new defined complexity measure [Formula: see text], of EEG signals or the ones of the mutual information transmission between different channels of EEGs were calculated to distinguish different consciousness levels for different brain functional states. All of the measures decreased with the following order of brain states: rest with eyes open, eyes closed, light sleep and deep sleep. They decreased during epileptic seizures. On the contrary, the averaged mutual information between different channels increased significantly during the epileptic seizure; there is no significant difference among the averaged mutual information for the subject resting with eyes open, closed, being in light sleep and in deep sleep. Thus, the former indexes seem to be promising candidates to characterize different consciousness levels, while the latter seems not.

scholarly journals Full Spectrum EEG Response to Eyes Open Versus Eyes Closed in Children and The Development of The Alpha Rhythm

2021 ◽  
Author(s):  
Joseph R. Isler ◽  
Nicolo Pini ◽  
Maristella Lucchini ◽  
Lauren C. Shuffrey ◽  
Santiago Morales ◽  
...  
Keyword(s):  
Electrode System ◽  
Full Spectrum ◽  
Eyes Closed ◽  
Sign Change ◽  
Eyes Open ◽  
Brain States ◽  
Eeg Power ◽  
The Difference ◽  
Open Versus ◽  
Electroencephalogram Eeg

Abstract This report examines spectrum-wide (1 to 100 Hz) differences in electroencephalogram (EEG) power between eyes open (EO) and eyes closed (EC) conditions in children. A high density (60 electrode) system was used to measure EEG power at 4, 5, 7, 9, and 11 years of age. Results showed spatial and frequency band differences as a function of age. Specifically, 1) the alpha peak shifts from 8 Hz at 4 years to 9 Hz at 11 years, 2) EC results in increased power at lower frequencies but decreased power at higher frequencies for all ages, 3) the sign change for the difference between EO and EC occurs in a narrow band of frequencies which changes across childhood, 4) at 4 and 5 years, EC increases lower frequency power most prominently over posterior regions; 5) in contrast, at all ages, EC decreases power above 30 Hz most prominently over anterior regions. These results extend previous findings to show EO/EC differences in higher frequencies and to the presence of developmental changes across childhood. This report demonstrates that the simple EO/EC task can provide important information about maturation of brain states and can be done with a very brief, minimal protocol.

scholarly journals EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)

2021 ◽  
Vol 11 (2) ◽  
pp. 214
Author(s):  
Anna Kaiser ◽  
Pascal-M. Aggensteiner ◽  
Martin Holtmann ◽  
Andreas Fallgatter ◽  
Marcel Romanos ◽  
...  
Keyword(s):  
Data Quality ◽  
Large Scale ◽  
Linear Models ◽  
Spectral Power ◽  
Control Group ◽  
Continuous Performance Task ◽  
Related Data ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.

scholarly journals Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 988
Author(s):  
Ho-Seung Cha ◽  
Chang-Hee Han ◽  
Chang-Hwan Im
Keyword(s):  
Resting State ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Mental States ◽  
Computer Interface ◽  
Practical Applications ◽  
Eeg Data ◽  
Brain States ◽  
Electroencephalogram Eeg ◽  
Eeg Features

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

scholarly journals Approximate Entropy of Brain Network in the Study of Hemispheric Differences

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1220
Author(s):  
Francesca Alù ◽  
Francesca Miraglia ◽  
Alessandro Orticoni ◽  
Elda Judica ◽  
Maria Cotelli ◽  
...  
Keyword(s):  
Brain Networks ◽  
Approximate Entropy ◽  
Brain Network ◽  
Hemispheric Differences ◽  
Eyes Closed ◽  
Age Related ◽  
Eeg Recordings ◽  
The Right ◽  
The Impact ◽  
Adult Volunteers

Human brain, a dynamic complex system, can be studied with different approaches, including linear and nonlinear ones. One of the nonlinear approaches widely used in electroencephalographic (EEG) analyses is the entropy, the measurement of disorder in a system. The present study investigates brain networks applying approximate entropy (ApEn) measure for assessing the hemispheric EEG differences; reproducibility and stability of ApEn data across separate recording sessions were evaluated. Twenty healthy adult volunteers were submitted to eyes-closed resting EEG recordings, for 80 recordings. Significant differences in the occipital region, with higher values of entropy in the left hemisphere than in the right one, show that the hemispheres become active with different intensities according to the performed function. Besides, the present methodology proved to be reproducible and stable, when carried out on relatively brief EEG epochs but also at a 1-week distance in a group of 36 subjects. Nonlinear approaches represent an interesting probe to study the dynamics of brain networks. ApEn technique might provide more insight into the pathophysiological processes underlying age-related brain disconnection as well as for monitoring the impact of pharmacological and rehabilitation treatments.

scholarly journals Human aging and loss variability brain networks based on electroencephalogram (EEG) data

2016 ◽  
Author(s):  
Octavio A. Lecona ◽  
Alejandro Hernández Fuentes ◽  
Ricardo Cruz Garduño ◽  
Bruno Méndez Ambrosio ◽  
Francisco F. De-Miguel ◽  
...  
Keyword(s):  
Brain Networks ◽  
Human Aging ◽  
Eeg Data ◽  
Electroencephalogram Eeg

scholarly journals A statistical model for brain networks inferred from large-scale electrophysiological signals

2017 ◽  
Vol 14 (128) ◽  
pp. 20160940 ◽  
Author(s):  
Catalina Obando ◽  
Fabrizio De Vico Fallani
Keyword(s):  
Statistical Model ◽  
Large Scale ◽  
Brain Networks ◽  
Biological Data ◽  
Current View ◽  
Exponential Random Graph Models ◽  
Eyes Closed ◽  
Graph Metrics ◽  
Neuroimaging Data ◽  
Eyes Open

Network science has been extensively developed to characterize the structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data represent one instance of a larger number of realizations with similar intrinsic topology. A modelling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. Here, we adopted a statistical model based on exponential random graph models (ERGMs) to reproduce brain networks, or connectomes, estimated by spectral coherence between high-density electroencephalographic (EEG) signals. ERGMs are made up by different local graph metrics, whereas the parameters weight the respective contribution in explaining the observed network. We validated this approach in a dataset of N = 108 healthy subjects during eyes-open (EO) and eyes-closed (EC) resting-state conditions. Results showed that the tendency to form triangles and stars, reflecting clustering and node centrality, better explained the global properties of the EEG connectomes than other combinations of graph metrics. In particular, the synthetic networks generated by this model configuration replicated the characteristic differences found in real brain networks, with EO eliciting significantly higher segregation in the alpha frequency band (8–13 Hz) than EC. Furthermore, the fitted ERGM parameter values provided complementary information showing that clustering connections are significantly more represented from EC to EO in the alpha range, but also in the beta band (14–29 Hz), which is known to play a crucial role in cortical processing of visual input and externally oriented attention. Taken together, these findings support the current view of the functional segregation and integration of the brain in terms of modules and hubs, and provide a statistical approach to extract new information on the (re)organizational mechanisms in healthy and diseased brains.

scholarly journals Detection of Burst Suppression Patterns in EEG Using Recurrence Rate

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhenhu Liang ◽  
Yinghua Wang ◽  
Yongshao Ren ◽  
Duan Li ◽  
Logan Voss ◽  
...  
Keyword(s):  
Recurrence Rate ◽  
Brain Activity ◽  
Approximate Entropy ◽  
Energy Operator ◽  
Burst Suppression ◽  
Multiple Comparison ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Nonlinear Energy ◽  
Selection Of

Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia. It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases. This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests. Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO). ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%, P=0.03). Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.

SYNCHRONIZATION MEASURES OF THE SCALP ELECTROENCEPHALOGRAM CAN DISCRIMINATE HEALTHY FROM ALZHEIMER'S SUBJECTS

2007 ◽  
Vol 17 (02) ◽  
pp. 61-69 ◽  
Author(s):  
MARK A. KRAMER ◽  
FEN-LEI CHANG ◽  
MAURICE E. COHEN ◽  
DONNA HUDSON ◽  
ANDREW J. SZERI
Keyword(s):  
Cognitive Impairment ◽  
Healthy Subjects ◽  
Phase Synchronization ◽  
Eyes Closed ◽  
Eeg Data ◽  
Two Measures ◽  
Left And Right ◽  
Scalp Electroencephalogram ◽  
Occipital Lobes ◽  
Electroencephalogram Eeg

Three synchronization measures are applied to scalp electroencephalogram (EEG) data collected from 20 patients diagnosed to have either: (1) no dementia, (2) mild cognitive impairment (MCI), or (3) Alzheimer's disease (AD). We apply the three synchronization measures — the phase synchronization, and two measures of nonlinear interdependency — to the data collected from awake patients resting with eyes closed. We show that the synchronization in potential between electrodes near the left and right occipital lobes provides a statistically significant discriminant between the healthy and AD subjects, and the MCI and AD subjects. None of the three measures appears able to distinguish between the healthy and MCI subjects, although MCI subjects show synchronization values intermediate between healthy subjects (with high synchronization values) and AD subjects (with low synchronization values) on average.

Sign in / Sign up

Export Citation Format

Share Document