Graph Theory at the Service of Electroencephalograms

Iakovidou, Nantia

doi:10.1089/brain.2016.0426

Cited by 14 publications

(16 citation statements)

References 96 publications

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“…The following eleven steps present the full pipeline for the construction of a functional brain network with graph theory of the EEG data using either the EEG sensor source method or space source method, which addresses RQ4. Previous studies have provided steps for either the sensor space method [40] or the source space method [53]. In the current study, we briefly describe the steps required for both methods focused on unweighted networks.…”

Section: Pipeline Steps For the Construction Of A Functional Brain Network Based On Eeg Datamentioning

confidence: 99%

“…The incoming node connections are referred to as "afferent," while the outgoing connections are referred to as "efferent" [102]. A node with a high degree of centrality is referred to as a "hub" [40], [151].…”

Section: Degree Centrality (K)mentioning

confidence: 99%

“…Hub is a node with more edges than any other node [102] and indicates the important brain regions that interact with other regions [40]. Provincial hubs are those connected to other nodes in the same module, whereas connector hubs are connected to nodes in other modules [152].…”

Section: Degree Centrality (K)mentioning

confidence: 99%

“…Three different types of connectivity are closely related: structural, functional, and effective connectivity [38], [39]. Structural connectivity encompasses the physical connections among neurons, known as "neuroanatomical" connections [40], which refers to the white matter connectivity in the brain. Functional connectivity is "the statistical interdependencies between physiological time series recorded from different brain regions" [41], [42].…”

Section: Introductionmentioning

confidence: 99%

“…Thresholding helps to simplify the complexity of the brain network calculations by eliminating weak, noisy, and insignificant edges from the network. Moreover, thresholding facilitates the definition of the null model for statistical comparison [4,40,80]. Selection of the threshold value significantly affects the network topology properties and the ability to detect differences among groups, ages, and genders.…”

mentioning

confidence: 99%

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A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

Ismail

Karwowski

2020

IEEE Access

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Graph theory analysis, a mathematical approach, has been applied in brain connectivity studies to explore the organization of network patterns. The computation of graph theory metrics enables the characterization of the stationary behavior of electroencephalogram (EEG) signals that cannot be explained by simple linear methods. The main purpose of this study was to systematically review the graph theory applications for mapping the functional connectivity of the EEG data in neuroergonomics. Moreover, this article proposes a pipeline for constructing an unweighted functional brain network from EEG data using both source and sensor methods. Out of 57 articles, our results show that graph theory metrics used to characterize EEG data have attracted increasing attention since 2006, with the highest frequency of publications in 2018. Most studies have focused on cognitive tasks in comparison with motor tasks. The mean phase coherence method, based on the "phase-locking value," was the most frequently used functional estimation technique in the reviewed studies. Furthermore, the unweighted functional brain network has received substantially more attention in the literature than the weighted network. The global clustering coefficient and characteristic path length were the most prevalent metrics for differentiating between global integration and local segregation, and the small-worldness property emerged as a compelling metric for the characterization of information processing. This review provides insight into the use of graph theory metrics to model functional brain connectivity in the context of neuroergonomics research.

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Section: Pipeline Steps For the Construction Of A Functional Brain Network Based On Eeg Datamentioning

confidence: 99%