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

Edmond Q. Wu, Zhiri Tang, Yuxuan Yao, Xu Yi Qiu, Ping Yu Deng, Pengwen Xiong, Aiguo Song, Li Min Zhu, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
JournalIEEE Transactions on Cybernetics
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Blood
  • Brain modeling
  • Brain workload
  • cognitive state
  • Fatigue
  • Gamma distribution
  • Matrix decomposition
  • Neurons
  • non-negative matrix network
  • Nonhomogeneous media
  • pilots' fatigue
  • Wigner-Ville distribution.

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