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Analysis of Optical Brain Signals Using Connectivity Graph Networks

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Machine Learning and Knowledge Extraction (CD-MAKE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12279))

Abstract

Graph network analysis (GNA) showed a remarkable role for understanding brain functions, but its application is mainly narrowed to fMRI research. Connectivity analysis (CA) is introduced as a signal-to-graph mapping in a time-causality framework. In this paper, we investigate the application of GNA/CA in fNIRS. To solve the inherent challenges of using CA, we also propose a novel metric: a maximum cross-lag magnitude (MCLM) that efficiently extracts major causality information. We tested MCLM in four types of cognitive activities (mental arithmetic, motor imagery, word generation, and brain workload) from 55 participants. CA/MCLM showed a compelling modeling capacity and revealed unexpected cross-subject network patterns. We found that motion imagery and mental arithmetic share a background network structure, and that the right prefrontal cortex, in AFp8, is an invariable destination for information flows in every stimuli and participant. Therefore, CA/MCLM-fNIRS showed potential for its use along with fMRI in clinical studies.

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Acknowledgments

This work is financially supported by the Research Council of Norway to the Project No. 273599, “Patient-Centric Engineering in Rehabilitation (PACER)”.

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Authors and Affiliations

  1. Department of Mechanical, Electronics and Chemical Engineering, Faculty of Technology, Art and Design, Oslo Metropolitan University, Oslo, Norway

    Marco Antonio Pinto-Orellana

  2. Department of Information Technology, Faculty of Technology, Art and Design, Oslo Metropolitan University, Oslo, Norway

    Hugo L. Hammer

  3. Simula Metropolitan Center, Oslo Metropolitan University, Oslo, Norway

    Hugo L. Hammer

Authors
  1. Marco Antonio Pinto-Orellana
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  2. Hugo L. Hammer
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Corresponding author

Correspondence to Marco Antonio Pinto-Orellana .

Editor information

Editors and Affiliations

  1. Human-Centered AI Lab, Institute for Medical Informatics, Statistics and Doumentation, Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. UAS St. Pölten, St. Pölten, Austria

    Peter Kieseberg

  3. Institute of Software Technology and Interactive Systems, Technical University of Vienna, Vienna, Austria

    A Min Tjoa

  4. SBA Research, Vienna, Austria

    Edgar Weippl

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Pinto-Orellana, M.A., Hammer, H.L. (2020). Analysis of Optical Brain Signals Using Connectivity Graph Networks. In: Holzinger, A., Kieseberg, P., Tjoa, A., Weippl, E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2020. Lecture Notes in Computer Science(), vol 12279. Springer, Cham. https://doi.org/10.1007/978-3-030-57321-8_27

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  • DOI: https://doi.org/10.1007/978-3-030-57321-8_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57320-1

  • Online ISBN: 978-3-030-57321-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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