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A Multilayer Network Approach for Studying Creative Ideation from EEG

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Book cover Brain Informatics (BI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11309))

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Abstract

The neural mechanisms underlying creative ideation are not clearly understood owing to the widespread cognitive processes involved in the brain. Current research states alpha band’s relation to creative ideation, as the most consistent finding. However, creative ideation appear at the signal level within multiple frequency bands and cross-frequency coupling phenomenon. To address this issue, we analyzed both within band and cross-frequency functional connectivity in a single framework using multilayer network. To further investigate the time evolution of creative thinking, we performed the analysis for three phases (early, middle and later). The experimental design used in this study consists of divergent thinking as an indicator of creativity where the subjects were instructed to give alternative uses of an object. As a control task, convergent thinking was used where the subjects were asked to list typical characteristics of an object. We evaluated global and nodal metrics (i.e., clustering coefficient, local efficiency, and nodal degree) for the three phases. Each metric was calculated separately for within band (intra layer) and cross-frequency (inter layer) connectivity. Paired t-test results showed significant difference in the later phase for both inter layer clustering coefficient and inter layer local efficiency. In nodal metrics, significant difference was observed in the later phase for intra layer degree and in all the phases for inter layer degree. The results from this study demonstrate that both the cross-frequency coupling and within-band connectivity can reveal more information regarding the neural processes related to creative ideation.

This work is supported by the startup grant of Prof. N. Thakor.

R. Bose and K. Ashutosh—Equal contribution.

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Author information

Authors and Affiliations

  1. Singapore Institute for Neurotechnology, National University of Singapore, Singapore, 117456, Singapore

    Rohit Bose, Junhua Li, Andrei Dragomir, Nitish Thakor & Anastasios Bezerianos

  2. Department of Electrical Engineering, Indian Institute of Technology, Bombay, 400076, India

    Kumar Ashutosh

Authors
  1. Rohit Bose
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  2. Kumar Ashutosh
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  3. Junhua Li
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  4. Andrei Dragomir
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  5. Nitish Thakor
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  6. Anastasios Bezerianos
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Corresponding author

Correspondence to Anastasios Bezerianos .

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Editors and Affiliations

  1. University of Texas at Arlington, Arlington, TX, USA

    Shouyi Wang

  2. University of Southern California, West Hollywood, USA

    Vicky Yamamoto

  3. Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA

    Jianzhong Su

  4. Maebashi Institute of Technology, Gunma, Japan

    Yang Yang

  5. The University of Texas at Arlington, Arlington, USA

    Erick Jones

  6. Louisiana Tech University, Arlington, TX, USA

    Leon Iasemidis

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Tom Mitchell

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Bose, R., Ashutosh, K., Li, J., Dragomir, A., Thakor, N., Bezerianos, A. (2018). A Multilayer Network Approach for Studying Creative Ideation from EEG. In: Wang, S., et al. Brain Informatics. BI 2018. Lecture Notes in Computer Science(), vol 11309. Springer, Cham. https://doi.org/10.1007/978-3-030-05587-5_28

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  • DOI: https://doi.org/10.1007/978-3-030-05587-5_28

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  • Online ISBN: 978-3-030-05587-5

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