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Brain-Connectivity Analysis to Differentiate Phasmophobic and Non-phasmophobic: An EEG Study

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Medical Image Understanding and Analysis (MIUA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12722))

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Abstract

Brain-connectivity refers to a pattern of functional or effective connectivity of distinct modules of human brain due to interactions between them. In this paper, the authors have attempted to conduct a brain connectivity based analysis to study the brain circuitry in subjects from their electroencephalographic (EEG) data, while they are engaged in playing a horror video game. The main motive of our work is to understand the differences in the effective connectivity among phasmophobic and non-phasmophobic subjects. In the present analysis, we propose a modified version of the causality test, named as Convergent Cross Mapping (CCM) to perform the analysis. The proposed CCM improves the performance of the standard CCM with an added feature of finding the possible direction of causation in terms of conditional entropy or maximum information transfer among the brain signal-sources. Experimental results and statistical analysis show that the proposed method shows superior efficacy in estimating the directed brain-connectivity as compared to the very well-known classical Granger Causality, classical CCM and other off-the-shelf brain-connectivity algorithms.

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Author information

Authors and Affiliations

  1. Artificial Intelligence Laboratory, Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India

    Suhita Karmakar, Dipayan Dewan, Lidia Ghosh, Abir Chowdhury & Amit Konar

  2. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

Authors
  1. Suhita Karmakar
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  2. Dipayan Dewan
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  3. Lidia Ghosh
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  4. Abir Chowdhury
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  5. Amit Konar
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  6. Atulya K. Nagar
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Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Bartłomiej W. Papież

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Mohammad Yaqub

  3. University of Oxford, Oxford, UK

    Jianbo Jiao

  4. University of Oxford, Oxford, UK

    Ana I. L. Namburete

  5. University of Oxford, Oxford, UK

    J. Alison Noble

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Karmakar, S., Dewan, D., Ghosh, L., Chowdhury, A., Konar, A., Nagar, A.K. (2021). Brain-Connectivity Analysis to Differentiate Phasmophobic and Non-phasmophobic: An EEG Study. In: Papież, B.W., Yaqub, M., Jiao, J., Namburete, A.I.L., Noble, J.A. (eds) Medical Image Understanding and Analysis. MIUA 2021. Lecture Notes in Computer Science(), vol 12722. Springer, Cham. https://doi.org/10.1007/978-3-030-80432-9_9

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  • DOI: https://doi.org/10.1007/978-3-030-80432-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80431-2

  • Online ISBN: 978-3-030-80432-9

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