Bayesian theory: Methods and applications

Sabzevari, Yaser; Eslamian, Saeid

doi:10.1016/b978-0-12-821285-1.00026-9

Cited by 1 publication

(2 citation statements)

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“…The likelihood function can be expressed as a product of conditional probabilities using the chain rule of probability [58]: where are the parents of variable Xj in the network, and θ j is the parameter of the conditional probability distribution of X j given its parents .…”

Section: Methodsmentioning

confidence: 99%

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Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification

Gangapuram,

Manian

2024

Preprint

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Brain responses related to working memory originate from distinct brain areas and oscillate at different frequencies. EEG signals with high temporal correlation can effectively capture these responses. Therefore, estimating the functional connectivity of EEG for working memory protocols in different frequency bands plays a significant role in analyzing the brain dynamics with increasing memory and cognitive loads, which remains largely unexplored. The present study introduces a Bayesian structure learning algorithm to learn the functional connectivity of EEG in sensor space. Next, the functional connectivity graphs are taken as input to the graph convolutional network to classify the working memory loads. The intrasubject (subject-specific) classification performed on 154 subjects for six different verbal working memory loads produced the highest classification accuracy of 96% and average classification accuracy of 89%, outperforming stateof- the-art classification models proposed in the literature. Furthermore, the proposed Bayesian structure learning algorithm is compared with state-of-the-art functional connectivity estimation methods through intersubject and intrasubject statistical analysis of variance. The results also show that the alpha and theta bands have better classification accuracy than the beta band.

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Section: Methodsmentioning

confidence: 99%

“…Where the likelihood of the data given the network structure is denoted by 𝑝(𝐷|𝐺), and prior probability of the network structure is denoted by 𝑝(𝐺). The likelihood function can be expressed as a product of conditional probabilities using the chain rule of probability [58]:…”

Section: Bayesian Information Criterionmentioning

confidence: 99%

Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification

Gangapuram,

Manian

2024

Preprint

View full text Add to dashboard Cite

show abstract

Bayesian theory: Methods and applications

Cited by 1 publication

References 10 publications

Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification

Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification

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