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

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.

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Reduced electroencephalogram complexity in postoperative delirium

The Journals of Gerontology Series A ◽

10.1093/gerona/glab352 ◽

2021 ◽

Author(s):

Sean Tanabe ◽

Maggie Parker ◽

Richard Lennertz ◽

Robert A Pearce ◽

Matthew I Banks ◽

...

Keyword(s):

Postoperative Delirium ◽

Primary Outcome ◽

Rating Scale ◽

Monocyte Chemoattractant Protein 1 ◽

Monocyte Chemoattractant ◽

Monocyte Chemoattractant Protein ◽

Eeg Data ◽

Before And After ◽

Electroencephalogram Eeg ◽

Multiple Comparison Correction

Abstract Delirium is associated with electroencephalogram (EEG) slowing and impairments in connectivity. We hypothesized that delirium would be accompanied by a reduction in the available cortical information (i.e. there is less information processing occurring), as measured by a surrogate, Lempil-Ziv Complexity (LZC), a measure of time-domain complexity. Two ongoing perioperative cohort studies (NCT03124303, NCT02926417) contributed EEG data from 91 patients before and after surgery; 89 participants were used in the analyses. After cleaning and filtering (0.1-50Hz), the perioperative change in LZC and LZC normalized (LZCn) to a phase-shuffled distribution were calculated. The primary outcome was the correlation of within-patient paired changes in delirium severity (Delirium Rating Scale-98 [DRS]) and LZC. Scalp-wide threshold free cluster enhancement was employed for multiple comparison correction. LZC negatively correlated with DRS in a scalp-wide manner (peak channel r 2=0.199, p<0.001). This whole brain effect remained for LZCn, though the correlations were weaker (peak channel r 2=0.076, p=0.010). Delirium diagnosis was similarly associated with decreases in LZC (peak channel p<0.001). For LZCn, the topological significance was constrained to the midline posterior regions (peak channel p=0.006). We found a negative correlation of LZC in the posterior and temporal regions with monocyte chemoattractant protein-1 (peak channel r 2=0.264, p<0.001, n=47) but not for LZCn. Complexity of the EEG signal fades proportionately to delirium severity implying reduced cortical information. Peripheral inflammation, as assessed by monocyte chemoattractant protein-1, does not entirely account for this effect, suggesting that additional pathogenic mechanisms are involved.

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Practical Artifact Removal Brain-Computer Interface System

Advances in Bioinformatics and Biomedical Engineering - Emerging Theory and Practice in Neuroprosthetics ◽

10.4018/978-1-4666-6094-6.ch013 ◽

2014 ◽

pp. 265-277

Author(s):

Wei-Yen Hsu

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Brain Computer Interface ◽

Computer Interface ◽

Support Vector ◽

Artifact Removal ◽

Eeg Data ◽

Interface System ◽

Electroencephalogram Eeg ◽

Bci System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

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Towards the Real-Life EEG Applications

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Cyber-Physical Systems for Social Applications ◽

10.4018/978-1-5225-7879-6.ch014 ◽

2019 ◽

pp. 305-317

Author(s):

Guangyi Ai

Keyword(s):

Data Analysis ◽

Real Life ◽

The Real ◽

Life Condition ◽

Research Fields ◽

Eeg Data ◽

Artifact Suppression ◽

Electroencephalogram Eeg ◽

Real Life Condition ◽

Brain Monitoring

Electroencephalogram (EEG) is one of the most popular approaches for brain monitoring in many research fields. While the detailed working flows for in-lab neuroscience-targeted EEG experiments conditions have been well established, carrying out EEG experiments under a real-life condition can be quite confusing because of various practical limitations. This chapter gives a brief overview of the practical issues and techniques that help real-life EEG experiments come into being, and the well-known artifact problems for EEG. As a guideline for performing a successful EEG data analysis with the low-electrode-density limitation of portable EEG devices, recently proposed techniques for artifact suppression or removal are briefly surveyed as well.

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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 ◽

10.3390/s20040988 ◽

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.

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EEG Data Mining Using PCA

Data Mining and Medical Knowledge Management ◽

10.4018/978-1-60566-218-3.ch008 ◽

2011 ◽

pp. 161-180 ◽

Cited By ~ 4

Author(s):

Lenka Lhotská ◽

Vladimír Krajca ◽

Jitka Mohylová ◽

Svojmil Petránek ◽

Václav Gerla

Keyword(s):

Neural Network ◽

Data Mining ◽

Principal Components ◽

The Neural Network ◽

Eeg Data ◽

Eigen Decomposition ◽

Electroencephalogram Eeg ◽

Epileptic Spike ◽

Eeg Signal Processing

This chapter deals with the application of principal components analysis (PCA) to the field of data mining in electroencephalogram (EEG) processing. The principal components are estimated from the signal by eigen decomposition of the covariance estimate of the input. Alternatively, they can be estimated by a neural network (NN) configured for extracting the first principal components. Instead of performing computationally complex operations for eigenvector estimation, the neural network can be trained to produce ordered first principal components. Possible applications include separation of different signal components for feature extraction in the field of EEG signal processing, adaptive segmentation, epileptic spike detection, and long-term EEG monitoring evaluation of patients in a coma.

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Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition

Neural Computation ◽

10.1162/neco_a_01183 ◽

2019 ◽

Vol 31 (5) ◽

pp. 919-942 ◽

Cited By ~ 5

Author(s):

Xian-Lun Tang ◽

Wei-Chang Ma ◽

De-Song Kong ◽

Wei Li

Keyword(s):

Supervised Learning ◽

Motor Imagery ◽

Adaptive Learning ◽

Recognition Accuracy ◽

Learning Rate ◽

Data Sets ◽

Adaptive Learning Rate ◽

Eeg Data ◽

Contrastive Divergence ◽

Electroencephalogram Eeg

Practical motor imagery electroencephalogram (EEG) data-based applications are limited by the waste of unlabeled samples in supervised learning and excessive time consumption in the pretraining period. A semisupervised deep stacking network with an adaptive learning rate strategy (SADSN) is proposed to solve the sample loss caused by supervised learning of EEG data and the extraction of manual features. The SADSN adopts the idea of an adaptive learning rate into a contrastive divergence (CD) algorithm to accelerate its convergence. Prior knowledge is introduced into the intermediary layer of the deep stacking network, and a restricted Boltzmann machine is trained by a semisupervised method in which the adjusting scope of the coefficient in learning rate is determined by performance analysis. Several EEG data sets are carried out to evaluate the performance of the proposed method. The results show that the recognition accuracy of SADSN is advanced with a more significant convergence rate and successfully classifies motor imagery.

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Human Brain Networks in Physiological Aging: A Graph Theoretical Analysis of Cortical Connectivity from EEG Data

Journal of Alzheimer s Disease ◽

10.3233/jad-140090 ◽

2014 ◽

Vol 41 (4) ◽

pp. 1239-1249 ◽

Cited By ~ 47

Author(s):

Fabrizio Vecchio ◽

Francesca Miraglia ◽

Placido Bramanti ◽

Paolo Maria Rossini

Keyword(s):

Theoretical Analysis ◽

Human Brain ◽

Brain Networks ◽

Cortical Connectivity ◽

Physiological Aging ◽

Graph Theoretical Analysis ◽

Eeg Data

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EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/6534041 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 6

Author(s):

Bingtao Zhang ◽

Tao Lei ◽

Hong Liu ◽

Hanshu Cai

Keyword(s):

Visual Inspection ◽

Sleep Stages ◽

Subjective Time ◽

Sleep Staging ◽

Large Bulk ◽

State Classification ◽

Eeg Data ◽

Electroencephalogram Eeg ◽

Eeg Features

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments.

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QUANTIFICATION OF DEPTH OF ANESTHESIA BY MEANS OF ADAPTIVE CALCULATION OF CORRELATION DIMENSION PARAMETERS

Fractals ◽

10.1142/s0218348x09004594 ◽

2009 ◽

Vol 17 (04) ◽

pp. 473-483

Author(s):

BEHZAD AHMADI ◽

BAHAREH ZAGHARI ◽

RASSOUL AMIRFATTAHI ◽

MOJTABA MANSOURI

Keyword(s):

Correlation Dimension ◽

Single Channel ◽

Drug Effects ◽

Eeg Signal ◽

Depth Of Anesthesia ◽

Anesthetic Drugs ◽

Eeg Data ◽

Prediction Probability ◽

Analysis Application ◽

Electroencephalogram Eeg

This paper proposes an approach for quantifying Depth of Anesthesia (DOA) based on correlation dimension (D2) of electroencephalogram (EEG). The single-channel EEG data was captured in both ICU and operating room while different anesthetic drugs, including propofol and isoflurane, were used. Correlation dimension was computed using various optimized parameters in order to achieve the maximum sensitivity to anesthetic drug effects and to enable real time computation. For better analysis, application of adaptive segmentation on EEG signal for estimating DOA was evaluated and compared to fixed segmentation, too. Prediction probability (PK) was used as a measure of correlation between the predictors and BIS index to evaluate the proposed methods. Appropriate correlation between DOA and correlation dimension is achieved while choosing (D2) parameters adaptively in comparison to fixed parameters due to the nonstationary nature of EEG signal.

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