Spectral and complexity features of the EEG changed by visual input in a case of subcortical stroke compared to healthy controls

Molnár, Márk; Csuhaj, Roland; Horváth, Szabolcs; Vastagh, Ildikó; Gaál, Zsófia Anna; Czigler, Balázs; Bálint, Andrea; Csikós, Dóra; Nagy, Zoltán

doi:10.1016/j.clinph.2005.12.022

Cited by 36 publications

(28 citation statements)

References 26 publications

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“…Considering the fact that the relative PSD is positively correlated with the energy (Elgendi et al 2011;Vecchio et al 2003), the results obtained suggest that brains affected by AD show a much slower physiological behavior. These abnormalities could reflect two different pathophysiological changes: the relative PSD decrease for higher frequencies could be related to alterations in cortico-cortical connections, whereas the increase for lower frequencies could be related to the lack of influence of subcortical cholinergic structures on cortical electrical activity (Jeong 2004;Molnár et al 2006;Moretti et al 2009). And the loss of cholinergic neurons in the basal forebrain projecting to Fig.…”

Section: Discussionmentioning

confidence: 99%

Power spectral density and coherence analysis of Alzheimer’s EEG

et al. 2014

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In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pairwise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.

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Section: Discussionmentioning

confidence: 99%

Power spectral density and coherence analysis of Alzheimer’s EEG

et al. 2014

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“…1,3,7,12,37,38 These abnormalities could reflect two different pathophysiological changes: the relative PSD decrease for higher frequencies could be related to alterations in cortico-cortical connections, whereas the increase for lower frequencies could be related to the lack of influence of subcortical cholinergic structures on cortical electrical activity. 12,39,40 Furthermore, less irregularity was observed by spectrum entropy and spectrum square entropy analysis in this work, which was intrinsically related to the reduced number of signal components. 41,42 These results were consistent with the other widely held view that the complexity of AD EEG, obtained by applying non-linear methods to study the brain activity in AD, was reduced.…”

Section: Discussionmentioning

confidence: 71%

Multiple feature extraction and classification of electroencephalograph signal for Alzheimers' with spectrum and bispectrum

Ruofan

Wang

et al. 2015

Chaos: An Interdisciplinary Journal of Nonlinear Science

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In this paper, we have combined experimental neurophysiologic recording and statistical analysis to investigate the nonlinear characteristic and the cognitive function of the brain. Spectrum and bispectrum analyses are proposed to extract multiple effective features of electroencephalograph (EEG) signals from Alzheimer's disease (AD) patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared to the control group, the relative power spectral density of AD group is significantly higher in the theta frequency band, while lower in the alpha frequency bands. In addition, median frequency of spectrum is decreased, and spectral entropy ratio of these two frequency bands undergoes drastic changes at the P3 electrode in the central-parietal brain region, implying that the electrophysiological behavior in AD brain is much slower and less irregular. In order to explore the nonlinear high order information, bispectral analysis which measures the complexity of phase-coupling is further applied to P3 electrode in the whole frequency band. It is demonstrated that less bispectral peaks appear and the amplitudes of peaks fall, suggesting a decrease of non-Gaussianity and nonlinearity of EEG in ADs. Notably, the application of this method to five brain regions shows higher concentration of the weighted center of bispectrum and lower complexity reflecting phase-coupling by bispectral entropy. Based on spectrum and bispectrum analyses, six efficient features are extracted and then applied to discriminate AD from the normal in the five brain regions. The classification results indicate that all these features could differentiate AD patients from the normal controls with a maximum accuracy of 90.2%. Particularly, different brain regions are sensitive to different features. Moreover, the optimal combination of features obtained by discriminant analysis may improve the classification accuracy. These results demonstrate the great promise for scape EEG spectral and bispectral features as a potential effective method for detection of AD, which may facilitate our understanding of the pathological mechanism of the disease.

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“…These techniques were and are used successfully for the quantitative assessment of the degree of EEG synchronization-reflecting on the complexity of the signal and that of the system (the brain) generating that signal-recorded in healthy subjects and in various neurological and psychiatric disorders such as Alzheimer's disease, seizures, Parkinson's disease, stroke, etc. (Stam, 2005;Molnár et al, 2006;Czigler et al, 2008, etc.). This approach was taken in the present study to analyze the effect of low dose alcohol on the EEG recorded during mental arithmetic.…”

Section: Introductionmentioning

confidence: 92%