"Scalable Gamma-Driven Multilayer Network for Brain Workload Detection " by Edmond Q. Wu, Zhiri Tang et al.
 

Scalable Gamma-Driven Multilayer Network for Brain Workload Detection Through Functional Near-Infrared Spectroscopy

Document Type

Article

Publication Date

11-1-2022

Abstract

This work proposes a scalable gamma non-negative matrix network (SGNMN), which uses a Poisson randomized Gamma factor analysis to obtain the neurons of the first layer of a network. These neurons obey Gamma distribution whose shape parameter infers the neurons of the next layer of the network and their related weights. Upsampling the connection weights follows a Dirichlet distribution. Downsampling hidden units obey Gamma distribution. This work performs up-down sampling on each layer to learn the parameters of SGNMN. Experimental results indicate that the width and depth of SGNMN are closely related, and a reasonable network structure for accurately detecting brain fatigue through functional near-infrared spectroscopy can be obtained by considering network width, depth, and parameters.

Identifier

85118585201 (Scopus)

Publication Title

IEEE Transactions on Cybernetics

External Full Text Location

https://doi.org/10.1109/TCYB.2021.3116964

e-ISSN

21682275

ISSN

21682267

PubMed ID

34705661

First Page

12464

Last Page

12478

Issue

11

Volume

52

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