Linear and Nonlinear Quantitative EEG Analysis

Wan, Baikun; Ming, Dong; Qi, Hongzhi; Xue, Zhaojun; Yin, Yong; Zhou, Zhongxing; Cheng, Lei

doi:10.1109/memb.2008.923957

Cited by 13 publications

(6 citation statements)

References 17 publications

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“…The EEG samples from the PRODEM-Austria database were all conducted in a uniform setting and according to a clinically predefined paradigm including both resting state and a cognitive task. The sample consisting of 79 EEG datasets from probable AD patients is, compared to the data in scientific literature, among the largest; a comparable or higher number has been reported in Wan et al ( 2008 ) (103 AD, 124 controls), Babiloni et al ( 2009 ) (73 AD, 69 subjects with MCI, 64 controls) and Babiloni et al ( 2006 ) (109 AD, 88 subjects with MCI, 69 controls).…”

Section: Discussionmentioning

confidence: 83%

“…Other than that, there is only a small number of studies that are directly comparable to this work since most studies compare groups (e.g., healthy controls versus AD patients) instead of correlating EEG synchrony markers with AD severity. However, a major share of these group comparisons suggested a decrease of EEG synchrony in resting state (e.g., Dauwels et al 2010a ; Locatelli et al 1998 ; Wada et al 1998 ; Anghinah et al 2000 ; Adler et al 2003 ; Jelles et al 2008 ; Babiloni et al 2009 ; Wan et al 2008 ; Stam et al 2003 ; Pijnenburg et al 2004 ; Kramer et al 2007 ), and an increase during cognitive tasks for MCI and in few cases also for AD patients as compared to controls (e.g., Jiang 2005b ; Jiang and Zheng 2006 ). This increase was attributed to compensatory mechanisms in the brain (cf.…”

Section: Discussionmentioning

confidence: 99%

“…( 2001 ), Wan et al. ( 2008 ), and Dauwels et al. ( 2010a ), phase synchrony measures in Stam et al.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying synchrony patterns in the EEG of Alzheimer’s patients with linear and non-linear connectivity markers

et al. 2015

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We analyzed the relation of several synchrony markers in the electroencephalogram (EEG) and Alzheimer’s disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores. The study sample consisted of 79 subjects diagnosed with probable AD. All subjects were participants in the PRODEM-Austria study. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. We employed quadratic least squares regression to describe the relation between MMSE and the EEG markers. Factor analysis was used for estimating a potentially lower number of unobserved synchrony factors. These common factors were then related to MMSE scores as well. Most markers displayed an initial increase of EEG synchrony with MMSE scores from 26 to 21 or 20, and a decrease below. This effect was most prominent during the cognitive task and may be owed to cerebral compensatory mechanisms. Factor analysis provided interesting insights in the synchrony structures and the first common factors were related to MMSE scores with coefficients of determination up to 0.433. We conclude that several of the proposed EEG markers are related to AD severity for the overall sample with a wide dispersion for individual subjects. Part of these fluctuations may be owed to fluctuations and day-to-day variability associated with MMSE measurements. Our study provides a systematic analysis of EEG synchrony based on a large and homogeneous sample. The results indicate that the individual markers capture different aspects of EEG synchrony and may reflect cerebral compensatory mechanisms in the early stages of AD.

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Section: Discussionmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

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Quantifying synchrony patterns in the EEG of Alzheimer’s patients with linear and non-linear connectivity markers

et al. 2015

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“…Linear analysis based on the Fourier transform provides information about the amplitude components of the EEG power spectrum. 19 In contrast, nonlinear EEG analysis consists of reconstruction from the EEG time series, an attractor of the underlying dynamic system, 20 with the aim of understanding the dynamic properties of the brain, 20 not often revealed by the Fourier analysis.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear EEG Analysis During Motor and Cognitive Tasks in Patients With Long COVID: A Dynamic Systems Approach

Morais,

Appelt,

Neto

et al. 2024

Clin EEG Neurosci

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Introduction. Nonlinear EEG provides information about dynamic properties of the brain. This study aimed to compare nonlinear EEG parameters estimated from patients with Long COVID in different cognitive and motor tasks. Materials and Methods. This 12-month prospective cohort study included 83 patients with Long COVID: 53 symptomatic and 30 asymptomatic. Brain electrical activity was evaluated by EEG in 4 situations: (1) at rest, (2) during the Trail Making Test Part A (TMT-A), (3) during the TMT Part B (TMT-B), and (4) during a coordination task: the Box and Blocks Test (BBT). Nonlinear EEG parameters were estimated in the time domain (activity and complexity). Assessments were made at 0 to 3, 3 to 6, and 6 to 12 months after inclusion. Results. There was a decrease in activity and complexity during the TMT-A and TMT-B, and an increase of these parameters during the BBT in both groups. There was an increase in activity at rest and during the TMT-A in the COVID-19 group at 0 to 3 months compared to the control, an increase in activity in the TMT-B in the COVID-19 group at 3 to 6 months compared to the control, and reduced activity and complexity at rest and during the TMT-A at 6 to 12 months compared to the control. Conclusion. The tasks followed a pattern of increased activity and complexity in cognitive tasks, which decreased during the coordination task. It was also observed that an increase in activity at rest and during cognitive tasks in the early stages, and reduced activity and complexity at rest and during cognitive tasks in the late phases of Long COVID.

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“…A decline in EEG irregularity was observed for AD patients using ApEn and auto mutual information (AMI). The low values revealed the dysfunction among different regions of the brain during Alzheimer’s [28, 29]. Slowing and complexity lowering of EEG signals were reported in MCI and mild AD patients, compared with healthy controls [30].…”

Section: Introductionmentioning

confidence: 99%

Exploration of time–frequency reassignment and homologous inter-hemispheric asymmetry analysis of MCI–AD brain activity

2019

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Background In this study, nonlinear based time–frequency (TF) and time domain investigations are employed for the analysis of electroencephalogram (EEG) signals of mild cognitive impairment–Alzheimer’s disease (MCI–AD) patients and healthy controls. This study attempts to comprehend the cognitive decline of MCI–AD under both resting and cognitive task conditions. Results Wavelet-based synchrosqueezing transform (SST) alleviates the smearing of energy observed in the spectrogram around the central frequencies in short-time Fourier transform (STFT), and continuous wavelet transform (CWT). A precise TF representation is assured due to the reassignment of scale variable to the frequency variable. It is discerned from the studies of time domain measures encompassing fractal dimension (FD) and approximate entropy (ApEn), that the parietal lobe is the most affected in MCI–AD under both resting and cognitive states. Alterations in asymmetry in the brain hemispheres are analysed using the homologous areas inter-hemispheric symmetry (HArS). Conclusion Time and time–frequency domain analysis of EEG signals have been used for distinguishing various brain states. Therefore, EEG analysis is highly useful for the screening of AD in its prodromal phase.

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Linear and Nonlinear Quantitative EEG Analysis

Cited by 13 publications

References 17 publications

Quantifying synchrony patterns in the EEG of Alzheimer’s patients with linear and non-linear connectivity markers

Quantifying synchrony patterns in the EEG of Alzheimer’s patients with linear and non-linear connectivity markers

Nonlinear EEG Analysis During Motor and Cognitive Tasks in Patients With Long COVID: A Dynamic Systems Approach

Exploration of time–frequency reassignment and homologous inter-hemispheric asymmetry analysis of MCI–AD brain activity

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