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
Recommended Citation
Wu, Edmond Q.; Tang, Zhiri; Yao, Yuxuan; Qiu, Xu Yi; Deng, Ping Yu; Xiong, Pengwen; Song, Aiguo; Zhu, Li Min; and Zhou, Meng Chu, "Scalable Gamma-Driven Multilayer Network for Brain Workload Detection Through Functional Near-Infrared Spectroscopy" (2022). Faculty Publications. 2571.
https://digitalcommons.njit.edu/fac_pubs/2571