scholarly journals Graph theoretical analysis based on EEG effective connectivity in ADHD children

2021 ◽  
Author(s):  
Mingkan Shen ◽  
Paul Wen ◽  
Yan Li ◽  
Bo Song
Keyword(s):  
Graph Theory ◽  
Effective Connectivity ◽  
Clustering Coefficient ◽  
Degree Centrality ◽  
Eeg Signals ◽  
Phase Slope ◽  
Delta Band ◽  
Eeg Data ◽  
Adhd Children ◽  
The Mean

Abstract This paper reports a new method to identify the ADHD children using EEG signals and effective connectivity techniques. In this study, the original EEG data is pre-filtered and divided into Delta, Theta, Alpha and Beta bands. And then, the effective connectivity graphs are constructed by applying independent component analysis, multivariate regression model and phase slope index. The measures of clustering coefficient, nodal efficiency and degree centrality in graph theory are used to extract features from these graphs. Statistical analysis based on the standard error of the mean are employed to evaluate the graph theory measures in each frequency band. The results show a decreased average clustering coefficient in delta band for ADHD subjects. Also, in delta band, the ADHD subjects have increased nodal efficiency and degree centrality in left forehead part and decreased in forehead middle.

scholarly journals Monitoring Effective Connectivity in the Preterm Brain: A Graph Approach to Study Maturation

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
M. Lavanga ◽  
O. De Wel ◽  
A. Caicedo ◽  
K. Jansen ◽  
A. Dereymaeker ◽  
...  
Keyword(s):  
Graph Theory ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Effective Connectivity ◽  
Small World ◽  
Clustering Coefficient ◽  
Postmenstrual Age ◽  
Eeg Connectivity ◽  
Preterm Babies ◽  
Age Prediction

In recent years, functional connectivity in the developmental science received increasing attention. Although it has been reported that the anatomical connectivity in the preterm brain develops dramatically during the last months of pregnancy, little is known about how functional and effective connectivity change with maturation. The present study investigated how effective connectivity in premature infants evolves. To assess it, we use EEG measurements and graph-theory methodologies. We recorded data from 25 preterm babies, who underwent long-EEG monitoring at least twice during their stay in the NICU. The recordings took place from 27 weeks postmenstrual age (PMA) until 42 weeks PMA. Results showed that the EEG-connectivity, assessed using graph-theory indices, moved from a small-world network to a random one, since the clustering coefficient increases and the path length decreases. This shift can be due to the development of the thalamocortical connections and long-range cortical connections. Based on the network indices, we developed different age-prediction models. The best result showed that it is possible to predict the age of the infant with a root mean-squared error (MSE) equal to 2.11 weeks. These results are similar to the ones reported in the literature for age prediction in preterm babies.

scholarly journals Robust Deep Network with Maximum Correntropy Criterion for Seizure Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yu Qi ◽  
Yueming Wang ◽  
Jianmin Zhang ◽  
Junming Zhu ◽  
Xiaoxiang Zheng
Keyword(s):  
Seizure Detection ◽  
Simultaneous Optimization ◽  
Eeg Signals ◽  
Deep Network ◽  
Scalp Eeg ◽  
Impulsive Noises ◽  
Eeg Data ◽  
The Mean ◽  
Maximum Correntropy Criterion

Effective seizure detection from long-term EEG is highly important for seizure diagnosis. Existing methods usually design the feature and classifier individually, while little work has been done for the simultaneous optimization of the two parts. This work proposes a deep network to jointly learn a feature and a classifier so that they could help each other to make the whole system optimal. To deal with the challenge of the impulsive noises and outliers caused by EMG artifacts in EEG signals, we formulate a robust stacked autoencoder (R-SAE) as a part of the network to learn an effective feature. In R-SAE, the maximum correntropy criterion (MCC) is proposed to reduce the effect of noise/outliers. Unlike the mean square error (MSE), the output of the new kernel MCC increases more slowly than that of MSE when the input goes away from the center. Thus, the effect of those noises/outliers positioned far away from the center can be suppressed. The proposed method is evaluated on six patients of 33.6 hours of scalp EEG data. Our method achieves a sensitivity of 100% and a specificity of 99%, which is promising for clinical applications.

scholarly journals Removal of EOG Artifacts from EEG Recordings Using Stationary Subspace Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hong Zeng ◽  
Aiguo Song
Keyword(s):  
Covariance Matrix ◽  
Blind Source Separation ◽  
Source Separation ◽  
Experimental Results ◽  
Eeg Signals ◽  
Subspace Analysis ◽  
Eeg Data ◽  
The Mean ◽  
Eeg Recordings ◽  
Ocular Artifacts

An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. The proposed approach first conducts the blind source separation on the raw EEG recording by the stationary subspace analysis (SSA) algorithm. Unlike the classic blind source separation algorithms, SSA is explicitly tailored to the understanding of distribution changes, where both the mean and the covariance matrix are taken into account. In addition, neither independency nor uncorrelation is required among the sources by SSA. Thereby, it can concentrate artifacts in fewer components than the representative blind source separation methods. Next, the components that are determined to be related to the ocular artifacts are projected back to be subtracted from EEG signals, producing the clean EEG data eventually. The experimental results on both the artificially contaminated EEG data and real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly nonstationary and the underlying sources cannot be assumed to be independent or uncorrelated.

scholarly journals Neural Correlation of Faradarmani Consciousness Field Mind Mediation: A Comparative Functional Connectivity and Graph Analysis

2021 ◽  
Author(s):  
Mohammad Ali Taheri ◽  
Fatemeh Modarresi-Asem ◽  
Noushin Nabavi ◽  
Parisa Maftoun ◽  
Farid Semsarha
Keyword(s):  
Graph Theory ◽  
Functional Connectivity ◽  
Brain Activity ◽  
Effective Connectivity ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Temporal Lobes ◽  
Neural Correlation ◽  
Graph Theory Analysis ◽  
The Brain

The study of the brain networks using analysis of electroencephalography (EEG) data based on statistical dependencies (functional connectivity) and mathematical graph theory concepts is common in neuroscience and cognitive sciences for examinations of patient and healthy individuals. The Consciousness Fields according to Taheri theory and applications in the optimization of system under study have been investigated in various studies. In this study, we examine the results of working with Faradarmani Consciousness Field (FCF) in the brain of Faradarmangars. Faradarmangars are one of the necessary components in mind mediation of the function of Faradarmani Consciousness Fields according to Taheri. For this purpose, the functional and effective connectivity and the corresponding brain graphs of EEG from the brain of Faradarmangars is compared with that of non Faradarmangar groups during FCF connection. According to the results of the present study, the brain of the Faradarmangars shows significant decreased activity in delta (BA8), beta2 (BA4/6/8/9/10/11/32/44/47) and beta3 (in 34 of 52 BA) frequency bands mainly in frontal lobe and after that in parietal and temporal lobes in the comparison with the non Faradarmangars. Moreover, the functional and effective connectivity analysis in the frontal network shows dominant multiple decreased connectivity mainly in the case of beta3 frequency band in all parts of the frontal network. On the other hand, the graph theory analysis of the Faradarmangar brain shows an increase in the activity of the O2-T5-F4-F3-FP2-F8 areas and significant decrease in the characteristic path length and increases in global efficiency, clustering coefficient and transitivity. In conclusion, the unique higher graph function efficiency and the reduction in the brain activity and connectivity during the Faradarmani Consciousness Field mind mediation, shown the passive and detector like function of the human brain in this task.

scholarly journals Altered Effective Connectivity in Migraine Patients During Emotional Stimuli: a Multi-frequency Magnetoencephalography Study.

2021 ◽  
Author(s):  
Jing Ren ◽  
Qun Yao ◽  
Minjie Tian ◽  
Feng Li ◽  
Yueqiu Chen ◽  
...  
Keyword(s):  
Negative Emotion ◽  
Brain Activity ◽  
Effective Connectivity ◽  
Temporal Cortex ◽  
Primary Headache ◽  
Clustering Coefficient ◽  
Emotional Stimuli ◽  
Delta Band ◽  
Wide Range ◽  
Graph Theory Analysis

Abstract Background: Migraine is a common and disabling primary headache associated with a wide range of psychiatric comorbidities. However, the mechanisms of emotion processing in migraine are not fully understood yet. The present study was designed to investigate the neural network during neutral, positive，and negative emotional stimuli in migraine suffers.Methods: We enrolled 24 migraine suffers and 24 age- and sex-matched controls in this study. Neuromagnetic brain activity was recorded by using a whole-head magnetoencephalography (MEG) system towards human faces expression pictures. MEG data were analyzed in the multi-frequency band of 1–100 Hz.Results: Migraine patients exhibited significantly enhanced effective connectivity from the prefrontal lobe to the temporal cortex during negative emotional stimuli in the gamma band(30-90Hz). Graph theory analysis revealed that patients had (1) an increased degree and clustering coefficient of connectivity in the delta band(1-4Hz) during positive emotional stimuli; (2) an increased degree of connectivity in the delta band(1-4Hz) during negative emotional stimuli.Conclusion: The results suggested individuals with migraine showed deviant effective connectivity when viewing human facial expressions in multi-frequency. The prefrontal-temporal pathway might be related to the altered negative emotion modulation in migraine. These findings may contribute to understanding the mechanism of the comorbidity of depression and anxiety in migraine and provide references for the comprehensive therapeutic plan.

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Damodar Reddy Edla ◽  
Shubham Dodia ◽  
Annushree Bablani ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Brain Computer Interface ◽  
Wavelet Packet ◽  
Stimulus Onset ◽  
Computer Interface ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Pass Filter ◽  
Eeg Data ◽  
Identification Test ◽  
Band Pass

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

Differences in Functional Brain Networks Between Subjective Cognitive Decline with and without Worry Groups: A Graph Theory Study from SILCODE

2021 ◽  
pp. 1-11
Author(s):  
Yi Liu ◽  
Zhuoyuan Li ◽  
Xueyan Jiang ◽  
Wenying Du ◽  
Xiaoqi Wang ◽  
...  
Keyword(s):  
Graph Theory ◽  
Cognitive Decline ◽  
Path Length ◽  
Brain Networks ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Subjective Cognitive Decline ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Functional Brain Networks

Background: Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown. Objective: In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry. Methods: A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied. Results: Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p <  0.001). The above results appeared in both the whole brain and DMN. Conclusion: There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nader Moharamzadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Effective Connectivity ◽  
Multivariate Time Series ◽  
Inference System ◽  
Eeg Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Intracranial Electroencephalography ◽  
Fuzzy Network ◽  
The Brain

Abstract The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

ASSESSING THE IMPACT OF CONGRUENT AND INCONGRUENT STIMULUS IN STROOP TASK, USING EVENT-RELATED POTENTIALS (ERP) IN PATIENTS WITH DEPRESSION

2018 ◽  
Vol 30 (05) ◽  
pp. 1850034
Author(s):  
Yeganeh Shahsavar ◽  
Majid Ghoshuni
Keyword(s):  
Stroop Task ◽  
Time Window ◽  
Event Related Potentials ◽  
Peak Time ◽  
Eeg Signals ◽  
Incongruent Stimulus ◽  
Related Potentials ◽  
Recording Channel ◽  
The Mean ◽  
The Impact

The main goal of this event-related potentials (ERPs) study was to assess the effects of stimulations in Stroop task in brain activities of patients with different degrees of depression. Eighteen patients (10 males, with the mean age [Formula: see text]) were asked to fill out Beck’s depression questionnaire. Electroencephalographic (EEG) signals of subjects were recorded in three channels (Pz, Cz, and Fz) during Stroop test. This test entailed 360 stimulations, which included 120 congruent, 120 incongruent, and 120 neutral stimulations. To analyze the data, 18 time features in each type of stimulus were extracted from the ERP components and the optimal features were selected. The correlation between the subjects’ scores in Beck’s depression questionnaires and the extracted time features in each recording channel was calculated in order to select the best features. Total area, and peak-to-peak time window in the Cz channel in both the congruent and incongruent stimulus showed significant correlation with Beck scores, with [Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text], respectively. Consequently, given the correlation between time features and the subjects’ Beck scores with different degrees of depression, it can be interpreted that in case of growth in degrees of depression, stimulations involving congruent images would produce more challenging interferences for the patients compared to incongruent stimulations which can be more effective in diagnosing the level of disorder.

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