scholarly journals Developmental Changes in EEG Phase Amplitude Coupling and Phase Preference over the First Three Years After Birth

2019 ◽  
Author(s):  
Michael G. Mariscal ◽  
April R. Levin ◽  
Laurel J. Gabard-Durnam ◽  
Helen Tager-Flusberg ◽  
Charles A. Nelson
Keyword(s):  
Early Childhood ◽  
Functional Connectivity ◽  
Clustering Methods ◽  
Eeg Data ◽  
Eyes Open ◽  
Alpha Beta ◽  
Infancy And Early Childhood ◽  
Phase Amplitude ◽  
The Brain ◽  
Phase Preference

AbstractThe coupling of the phase of slower electrophysiological oscillations with the amplitude of faster oscillations, termed phase-amplitude coupling (PAC), is thought to facilitate dynamic connectivity in the brain. Though the brain undergoes dramatic changes in connectivity during the first few years of life, how PAC changes through this developmental period has not been studied. Here, we examined PAC through electroencephalography (EEG) data collected longitudinally during an awake, eyes-open EEG collection paradigm in 98 children between the ages of 3 months and 3 years. We implement a novel technique developed for capturing both PAC strength and phase preference (i.e., where in the slower oscillation waveform the faster oscillation shows increased amplitude) simultaneously, and employed non-parametric clustering methods to evaluate our metrics across a range of frequency pairs and electrode locations. We found that frontal and occipital PAC, primarily between the alpha-beta and gamma frequencies, increased from early infancy to early childhood (p = 1.35 x 10-5). Additionally, we found frontal gamma coupled with the trough of the alpha-beta waveform, while occipital gamma coupled with the peak of the alpha-beta waveform. This opposing trend may reflect each region’s specialization towards feedback or feedforward processing, respectively.Significance StatementThe brain undergoes significant changes in functional connectivity during infancy and early childhood, enabling the emergence of higher-level cognition. Phase-amplitude coupling (PAC) is thought to support the functional connectivity of the brain. Here, we find PAC increases from 3 months to 3 years of age. We additionally report the frontal and occipital brain areas show opposing forms of PAC; this difference could facilitate each region’s tendency towards bottom-up or top-down processing.

scholarly journals EEG data might help identify children at risk for social anxiety

2021 ◽  
Keyword(s):  
Gamma Delta ◽  
Related Activity ◽  
Frequency Bands ◽  
Non Invasive ◽  
Eeg Power Spectrum ◽  
Eeg Data ◽  
Invasive Method ◽  
Alpha Beta ◽  
Beta Frequency ◽  
The Brain

Electroencephalography (EEG) is a non-invasive method to monitor the electrical activity of the brain. There are five main broad frequency bands in the EEG power spectrum: alpha, beta, gamma, delta and theta. Data suggest that EEG-derived delta–beta coupling — indicating related activity in the delta and beta frequency bands — might serve as a marker of emotion regulation.

scholarly journals Putting the “dynamic” back into dynamic functional connectivity

2017 ◽  
Author(s):  
Stewart Heitmann ◽  
Michael Breakspear
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Large Scale ◽  
Time Series Data ◽  
Temporal Structure ◽  
Series Data ◽  
Dynamic Functional Connectivity ◽  
Time Resolved ◽  
Phase Amplitude ◽  
The Brain

AbstractThe study of fluctuations in time-resolved functional connectivity is a topic of substantial current interest. As the term “dynamic functional connectivity” implies, such fluctuations are believed to arise from dynamics in the neuronal systems generating these signals. While considerable activity currently attends to methodological and statistical issues regarding dynamic functional connectivity, less attention has been paid toward its candidate causes. Here, we review candidate scenarios for dynamic (functional) connectivity that arise in dynamical systems with two or more subsystems; generalized synchronization, itinerancy (a form of metastability), and multistability. Each of these scenarios arise under different configurations of local dynamics and inter-system coupling: We show how they generate time series data with nonlinear and/or non-stationary multivariate statistics. The key issue is that time series generated by coupled nonlinear systems contain a richer temporal structure than matched multivariate (linear) stochastic processes. In turn, this temporal structure yields many of the phenomena proposed as important to large-scale communication and computation in the brain, such as phase-amplitude coupling, complexity and flexibility. The code for simulating these dynamics is available in a freeware software platform, the “Brain Dynamics Toolbox”.

scholarly journals Classification of Resting-State Status Based on Sample Entropy and Power Spectrum of Electroencephalography (EEG)

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ahmed M. A. Mohamed ◽  
Osman N. Uçan ◽  
Oğuz Bayat ◽  
Adil Deniz Duru
Keyword(s):  
Resting State ◽  
Sample Entropy ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Frequency Bands ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open ◽  
The Brain

An electroencephalogram (EEG) is a significant source of diagnosing brain issues. It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects. This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal. The Fast Fourier Transform (FFT) and sample entropy (SE) of each sensor are computed as methods of feature extraction. Six classifiers, including logistic regression (LR), K-Nearest Neighbors (KNN), linear discriminant (LD), decision tree (DT), support vector machine (SVM), and Gaussian Naive Bayes (GNB) are used to discriminate the resting states of the brain based on the extracted features. EEG data were epoched with one-second-length windows, and they were used to compute the features to classify EO and EC conditions. Results showed that the LR and SVM classifiers had the highest average classification accuracy (97%). Accuracies of LD, KNN, and DT were 95%, 93%, and 92%, respectively. GNB gained the least accuracy (86%) when conventional frequency bands were used. On the other hand, when SE was used, the average accuracies of SVM, LD, LR, GNB, KNN, and DT algorithms were 92% 90%, 89%, 89%, 86%, and 86%, respectively.

scholarly journals RECONHECIMENTO DE ESTADOS DOS OLHOS UTILIZANDO MÁQUINAS DE APRENDIZADO PROFUNDO A PARTIR DE ONDAS CEREBRAIS

2019 ◽  
Vol 9 (1) ◽  
pp. 41-57 ◽  
Keyword(s):  
Short Term Memory ◽  
Human Cognition ◽  
Short Term ◽  
Data Set ◽  
Eeg Data ◽  
Eyes Open ◽  
Long Short Term Memory ◽  
Base Data ◽  
The Brain

The brain-computer interface is one of the emerging fields of human-computer interaction due to its broad spectrum of applications, especially those that deal with human cognition. In this work, electroencephalography (EEG) is used as base data for classifying the state of the eyes (open or closed) by applying Long Short-Term Memory (LSTM) networks and variants. For benchmarking purposes, the EEG data set with the eye state record was used, available in the Machine Learning repository at UCI. The results obtained indicated that the LSTM and GRU bidirectional cells models are applicable to the classification of the data, presenting an accuracy greater than 95%, and that its performance is good compared to the more expensive models computationally.

scholarly journals Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification

2018 ◽  
Vol 28 (06) ◽  
pp. 1750064 ◽  
Author(s):  
Vitaly Schetinin ◽  
Livija Jakaite ◽  
Ndifreke Nyah ◽  
Dusica Novakovic ◽  
Wojtek Krzanowski
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Identification Accuracy ◽  
Group Method ◽  
Autoregressive Modeling ◽  
Eeg Signals ◽  
Person Identification ◽  
Benchmark Data ◽  
Eeg Data ◽  
The Brain

The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique “brain print”, which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant ([Formula: see text]) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.

scholarly journals Effect of levodopa on human brain connectome in Parkinsons disease

2021 ◽  
Author(s):  
Sajjad Farashi ◽  
Mojtaba Khazaei
Keyword(s):  
Brain Connectivity ◽  
Gamma Band ◽  
Beta Band ◽  
Positive Effects ◽  
Eyes Closed ◽  
Brain Connectome ◽  
Eeg Data ◽  
Eyes Open ◽  
Gamma Band Activity ◽  
The Brain

Levodopa-based drugs are widely used for mitigating the complications induced by PD. Despite the positive effects, several issues regarding the way that levodopa changes brain activities have remained unclear. Methods-A combined strategy using EEG data and graph theory was used for investigating how levodopa changed connectome and processing hubs of the brain during resting-state. Obtained results were subjected to ANOVA test and multiple-comparison post-hoc correction procedure. Results: Results showed that graph topology of PD patients was not significantly different with the healthy group during eyes-closed condition while in eyes-open condition statistical significant differences were found. The main effect of levodopa medication was observed for gamma-band activity of the brain in which levodopa changed the brain connectome toward a star-like topology. Considering the beta subband of EEG data, graph leaf number increased following levodopa medication in PD patients. Enhanced brain connectivity in gamma band and reduced beta band connections in basal ganglia were also observed after levodopa medication. Furthermore, source localization using dipole fitting showed that levodopa prescription suppressed the activity of collateral trigone. Conclusion: Our combined EEG and graph analysis showed that levodopa medication changed the brain connectome, especially in the high-frequency range of EEG (beta and gamma).

scholarly journals Putting the “dynamic” back into dynamic functional connectivity

2018 ◽  
Vol 2 (2) ◽  
pp. 150-174 ◽  
Author(s):  
Stewart Heitmann ◽  
Michael Breakspear
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Large Scale ◽  
Time Series Data ◽  
Temporal Structure ◽  
Series Data ◽  
Dynamic Functional Connectivity ◽  
Time Resolved ◽  
Phase Amplitude ◽  
The Brain

The study of fluctuations in time-resolved functional connectivity is a topic of substantial current interest. As the term “dynamic functional connectivity” implies, such fluctuations are believed to arise from dynamics in the neuronal systems generating these signals. While considerable activity currently attends to methodological and statistical issues regarding dynamic functional connectivity, less attention has been paid toward its candidate causes. Here, we review candidate scenarios for dynamic (functional) connectivity that arise in dynamical systems with two or more subsystems; generalized synchronization, itinerancy (a form of metastability), and multistability. Each of these scenarios arises under different configurations of local dynamics and intersystem coupling: We show how they generate time series data with nonlinear and/or nonstationary multivariate statistics. The key issue is that time series generated by coupled nonlinear systems contain a richer temporal structure than matched multivariate (linear) stochastic processes. In turn, this temporal structure yields many of the phenomena proposed as important to large-scale communication and computation in the brain, such as phase-amplitude coupling, complexity, and flexibility. The code for simulating these dynamics is available in a freeware software platform, the Brain Dynamics Toolbox.

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