Examination of the brain areas related to cognitive performance during the stroop task using deep neural network

Tomohiro Nishikawa, Yushi Hashimoto, Kosei Minami, Keiichi Watanuki, Kazunori Kaede, Keiichi Muramatsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To examine brain areas related to the cognitive load condition during the Stroop task, we proposed a method using a Deep Neural Network (DNN). We acquired cerebral blood flow data in congruent and incongruent tasks by near-infrared spectroscopy (NIRS) equipped with 22 ch. The data were used to train a DNN, and the influence of each factor on the output was evaluated. Our DNN model consists of independent input layers for each channel of NIRS, as well as fully-connected hidden layers and output layers. Our results suggest that the medial prefrontal cortex (focusing on cognition) and the left inferior frontal gyrus (focusing on language processing) were involved in the cognitive load during the Stroop task. These results in the Stroop task were consistent. Therefore, the proposed method’s utility was confirmed.

Original languageEnglish
Title of host publicationAdvances in Affective and Pleasurable Design - Proceedings of the AHFE 2018 International Conference on Affective and Pleasurable Design, 2018
EditorsShuichi Fukuda
PublisherSpringer Verlag
Pages94-101
Number of pages8
ISBN (Print)9783319949437
DOIs
Publication statusPublished - 2019
Externally publishedYes
EventAHFE International Conference on Affective and Pleasurable Design, 2018 - Orlando, United States
Duration: 2018 Jul 212018 Jul 25

Publication series

NameAdvances in Intelligent Systems and Computing
Volume774
ISSN (Print)2194-5357

Conference

ConferenceAHFE International Conference on Affective and Pleasurable Design, 2018
CountryUnited States
CityOrlando
Period18/7/2118/7/25

Keywords

  • Cognitive load
  • Deep Neural Network
  • Near-infrared spectroscopy
  • Stroop task

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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