Capturing Cognition via EEG-Based Functional Brain Networks

Shovon, Md. Hedayetul Islam; Nandagopal, D; Cocks, Bernadine; Ramasamy, Vijayalakshmi

doi:10.1007/978-981-10-3957-7_8

Cited by 4 publications

(3 citation statements)

References 50 publications

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“…The use of new EEG analysis approaches beyond ERPs analyses may also help to expand our understanding of the developmental processes underlying the development of IC processes. For example, the analyses of functional brain connectivity may shed light on the development of neural networks parallel to the enhancement of IC skills (Park and Friston, 1979;Shovon et al, 2017), whereas the multivariate pattern analyses could provide richer information about brain processing by identifying neural patterns of activation related to IC (Ashton et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Predicting behavioral and brain markers of inhibitory control at preschool age from early measures of executive attention

et al. 2023

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BackgroundInhibitory control (IC) is the ability to prevent prepotent responses when inappropriate. Longitudinal research on IC development has mainly focused on early childhood and adolescence, while research on IC development in the first years of life is still scarce. To address this gap in the literature, we explored the association between executive attention (EA) and elementary forms of IC in infancy and toddlerhood, with individual differences in IC later at 5 years of age.MethodWe conducted a five-wave longitudinal study in which children’s EA and IC (n = 96) were tested at the age of 9 and 16 months and 2, 3, and 5 years. Children performed various age-appropriate EA and IC tasks in each wave, measuring inhibition of attention, endogenous control of attention, inhibition of the response, and conflict inhibition. At 5 years of age, IC was measured with a Go/No-go task while recording event-related potentials. After correlation analyses, structural equation model analyses were performed to predict IC at 5 years of age from EA and early IC measures.ResultsThe results revealed that EA at 9 months predicted IC measures at 2 years of age. Likewise, measures of IC at 2 years predicted performance on the Go/No-go task at behavioral and neural levels. No direct association was found between EA at 9 months and IC at 5 years of age. We further observed that some EA and IC measures were not associated across time.ConclusionAs we expected, EA skills in infancy and toddlerhood were related to better performance of children on IC tasks, toghether with a more mature inhibition-related brain functioning. Altogether, the results indicate that IC in early childhood could be predicted from EA and IC at 9 months and 2 years of age and suggest that the early emergence of IC relies on the development of particular EA and basic IC skills. However, some discontinuities in the longitudinal development of IC are observed in the first 5 years of life. These findings provide further support for the hierarchical model of IC development.

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Section: Discussionmentioning

confidence: 99%

Predicting behavioral and brain markers of inhibitory control at preschool age from early measures of executive attention

et al. 2023

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“…The NTE from y→x is not equal to NTE from x→y and NTE is in the range of 0 and 1. If the value of NTE is 0 that means no transfer of information and if the value is 1 than the information transfer is maximum [13].…”

Section: Methodsmentioning

confidence: 99%

“…TE uses the past activity of both variables to estimate the amount of activity of a system irrespective of interaction model. This property of TE allows researchers to apply it to various applications such as identifying information transfer between auditory cortical data [10], localization of epileptic patients focus [11], the effect of heart rate on breath rate [12], and information flow patterns in various driving states [13]. Other brain connectivity measures such as partial directed coherence (PDC) and directed transfer function (DTF) was used in the literature to evaluate the performance during meditation [14,15].…”

Section: Introductionmentioning

confidence: 99%

Connectivity Analysis Using Functional Brain Networks to Evaluate Cognitive Activity during 3D Modelling

Baig

Kavakli

2019

Brain Sciences

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Modelling 3D objects in CAD software requires special skills which require a novice user to undergo a series of training exercises to obtain. To minimize the training time for a novice user, the user-dependent factors must be studied. we have presented a comparative analysis of novice/expert information flow patterns. We have used Normalized Transfer Entropy (NTE) and Electroencephalogram (EEG) to investigate the differences. The experiment was divided into three cognitive states i.e., rest, drawing, and manipulation. We applied classification algorithms on NTE matrices and graph theory measures to see the effectiveness of NTE. The results revealed that the experts show approximately the same cognitive activation in drawing and manipulation states, whereas for novices the brain activation is more in manipulation state than drawing state. The hemisphere- and lobe-wise analysis showed that expert users have developed an ability to control the information flow in various brain regions. On the other hand, novice users have shown a continuous increase in information flow activity in almost all regions when doing drawing and manipulation tasks. A classification accuracy of more than 90% was achieved with a simple K-nearest neighbors (k-NN) to classify novice and expert users. The results showed that the proposed technique can be used to develop adaptive 3D modelling systems.

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Characterizing EEG Electrodes in Directed Functional Brain Networks Using Normalized Transfer Entropy and PageRank

Suresh

Ramasamy

Daniel

et al. 2021

Intelligent Systems Reference Library

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Capturing Cognition via EEG-Based Functional Brain Networks

Cited by 4 publications

References 50 publications

Predicting behavioral and brain markers of inhibitory control at preschool age from early measures of executive attention

Predicting behavioral and brain markers of inhibitory control at preschool age from early measures of executive attention

Connectivity Analysis Using Functional Brain Networks to Evaluate Cognitive Activity during 3D Modelling

Characterizing EEG Electrodes in Directed Functional Brain Networks Using Normalized Transfer Entropy and PageRank

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