Intertrial coherence and causal interaction among independent EEG components

Zervakis, Michalis; Michalopoulos, Kostas; Iordanidou, Vasiliki; Sakkalis, Vangelis

doi:10.1016/j.jneumeth.2011.02.001

Cited by 28 publications

(16 citation statements)

References 51 publications

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“…The analysis was based in gICA, to extract the neuronal sources related to resting state, and inverse solution methods to locate the neuronal sources in the brain cortex. The concatenation of subject data for gICA analysis has been applied to study Mild Cognitive Impairment [33], Alzheimer disease [34] and Attention-Deficit Hyperactivity Disorder [35].…”

Section: Introductionmentioning

confidence: 99%

Precuneus Failures in Subjects of the PSEN1 E280A Family at Risk of Developing Alzheimer’s Disease Detected Using Quantitative Electroencephalography

Villa-Ochoa

Alonso

Duque

et al. 2017

JAD

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Background: Presenilin-1 (PSEN1) mutations are the most common cause of familial early onset Alzheimer's Disease (AD). The PSEN1 E280A (E280A) mutation has an autosomal dominant inheritance and is involved in the production of amyloid-β. In Antioquia, Colombia, there is the largest family group of carriers with E280A mutation. The study of mutation carriers provides an unique opportunity to identify brain changes in stages previous to AD. Electroencephalography (EEG) is a low cost and minimally invasiveness technique that enables the following of brain changes in AD.Objective: To examine how previous reported differences in EEG for Theta and Alpha-2 rhythms in E280A subjects are related to specific regions in cortex and could be tracked across different ages. Methods: EEG signals were acquired during resting state from non-carriers and carriers, asymptomatic and symptomatic, subjects from E280A kindred from Antioquia, Colombia. Independent Component Analysis and inverse solution methods were used to locate brain regions related to differences in Theta and Alpha-2 bands.Results: Independent Component Analysis helps to identify two components, mainly related to the Precuneus, were the differences in Theta and Alpha-2 exist simultaneously at asymptomatic and symptomatic stage. When a ratio between Theta and Alpha-2 is used significant correlations exist with age and a composite cognitive scale. Conclusion:Theta and Alpha-2 rhythms are altered in E280A subjects. The alterations are possible to track at Precuneus regions using EEG, ICA and inverse solution methods.

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Section: Introductionmentioning

confidence: 99%

Precuneus Failures in Subjects of the PSEN1 E280A Family at Risk of Developing Alzheimer’s Disease Detected Using Quantitative Electroencephalography

Villa-Ochoa

Alonso

Duque

et al. 2017

JAD

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“…The P300 component is typically elicited approximately 300 ms after each infrequent target stimulus, with reflecting the context updating and the categorization of relevant tasks [14–16]. Parameters extracted from ERP signals are of clinical interest because they are useful in differentiating the healthy controls from cognitive impairment patients [8, 11, 17].…”

Section: Introductionmentioning

confidence: 99%

Identifying Patients with Poststroke Mild Cognitive Impairment by Pattern Recognition of Working Memory Load-Related ERP

Yan

Wei

2013

Computational and Mathematical Methods in Medicine

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The early detection of subjects with probable cognitive deficits is crucial for effective appliance of treatment strategies. This paper explored a methodology used to discriminate between evoked related potential signals of stroke patients and their matched control subjects in a visual working memory paradigm. The proposed algorithm, which combined independent component analysis and orthogonal empirical mode decomposition, was applied to extract independent sources. Four types of target stimulus features including P300 peak latency, P300 peak amplitude, root mean square, and theta frequency band power were chosen. Evolutionary multiple kernel support vector machine (EMK-SVM) based on genetic programming was investigated to classify stroke patients and healthy controls. Based on 5-fold cross-validation runs, EMK-SVM provided better classification performance compared with other state-of-the-art algorithms. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the maximum classification accuracies of 91.76% and 82.23% for 0-back and 1-back tasks, respectively. Overall, the experimental results showed that the proposed method was effective. The approach in this study may eventually lead to a reliable tool for identifying suitable brain impairment candidates and assessing cognitive function.

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“…The assumption that all the bands display the same topography is seriously challenged by findings based on other studies [4], [9]. In our case we want to take under consideration the relationships between the different bands and use this information for better modeling of the data.…”

Section: B Spatio-temporal Modeling Of the Topographymentioning

confidence: 97%

“…Limiting the analysis on a single electrode misses information that is collected in other sites and disregards the fact that different aspects of the underlying brain activity are manifested in multiple locations. Multivariate techniques as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been employed in an effort to alleviate this problem [3], [4]. K Different methodologies have been used in order to extract features able to discriminate the brain responses to different tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the band specific evolution of the topographic maps, results from the analysis of temporal features of the EEG and especially from the application of ICA, indicate that activity attributed to a certain band arises in certain electrodes in conjunction or as a response to other band activations in different locations [4], [9]. The ensemble of these activations constitutes an interacting network that characterizes the functional processing that takes place.…”

Section: Introductionmentioning

confidence: 99%

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Using Dynamic Bayesian Networks for modeling EEG topographic sequences

Michalopoulos¹,

Bourbakis²

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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In this work we present a methodology for modeling the trajectory of EEG topography over time, using Dynamic Bayesian Networks (DBNs). Based on the microstate model we are using DBNs to model the evolution of the EEG topography. Analysis of the microstate model is being usually limited in the wide band signal or an isolated band. We are using Coupled Hidden Markov Models (CHMM) and a two level influence model in order to model the temporal evolution and the coupling of the topography states in three bands, delta, theta and alpha. We are applying this methodology for the classification of target and non-target single trial from a visual detection task. The results indicate that taking under consideration the interaction among the different bands improves the classification of single trials.

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Intertrial coherence and causal interaction among independent EEG components

Cited by 28 publications

References 51 publications

Precuneus Failures in Subjects of the PSEN1 E280A Family at Risk of Developing Alzheimer’s Disease Detected Using Quantitative Electroencephalography

Precuneus Failures in Subjects of the PSEN1 E280A Family at Risk of Developing Alzheimer’s Disease Detected Using Quantitative Electroencephalography

Identifying Patients with Poststroke Mild Cognitive Impairment by Pattern Recognition of Working Memory Load-Related ERP

Using Dynamic Bayesian Networks for modeling EEG topographic sequences

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