Deep leaning-based approach for mental workload discrimination from multi-channel fNIRS

Thi Kieu Khanh Ho, Jeonghwan Gwak, Chang Min Park, Ashish Khare, Jong In Song

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

As a non-invasive optical neuroimaging technique, functional near infrared spectroscopy (fNIRS) is currently used to assess brain dynamics during the performance of complex works and everyday tasks. However, the deep learning approaches to distinguish stress levels based on the changes of hemoglobin concentrations have not yet been extensively investigated. In this paper, we evaluated the efficiencies of advanced methods differentiating the rest and task periods during stroop task experiments. First, we explored that the apparent changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations associated with two mental stages did exist across each participant. Then, a novel discrimination framework was studied. Deep learning approaches, including convolutional neural network (CNN), deep belief networks (DBN), have enabled better classification accuracies of 84.26 ± 9.10% and 65.43 ± 1.59% as our preliminary study.

Original languageEnglish
Title of host publicationRecent Trends in Communication, Computing, and Electronics - Select Proceedings of IC3E 2018
EditorsIshwar K. Sethi, Ashish Khare, Nar Singh, Uma Shankar Tiwary
PublisherSpringer Verlag
Pages431-440
Number of pages10
ISBN (Print)9789811326844
DOIs
StatePublished - 2019
EventInternational Conference on Emerging Trends in Communication, Computing and Electronics, IC3E 2018 - [city ]Allahabad, India
Duration: 13 Apr 201915 Apr 2019

Publication series

NameLecture Notes in Electrical Engineering
Volume524
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Other

OtherInternational Conference on Emerging Trends in Communication, Computing and Electronics, IC3E 2018
Country/TerritoryIndia
City[city ]Allahabad
Period13/04/1915/04/19

Keywords

  • Convolutional neural networks
  • Deep belief networks
  • Functional near infrared spectroscopy
  • Stroop task experiments

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