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Data-driven Koopman operator approach for computational neuroscience

  • Natasza MarrouchEmail author
  • Joanna Slawinska
  • Dimitrios Giannakis
  • Heather L. Read
Open Access
Article
  • 107 Downloads

Abstract

This article presents a novel, nonlinear, data-driven signal processing method, which can help neuroscience researchers visualize and understand complex dynamical patterns in both time and space. Specifically, we present applications of a Koopman operator approach for eigendecomposition of electrophysiological signals into orthogonal, coherent components and examine their associated spatiotemporal dynamics. This approach thus provides enhanced capabilities over conventional computational neuroscience tools restricted to analyzing signals in either the time or space domains. This is achieved via machine learning and kernel methods for data-driven approximation of skew-product dynamical systems. The approximations successfully converge to theoretical values in the limit of long embedding windows. First, we describe the method, then using electrocorticographic (ECoG) data from a mismatch negativity experiment, we extract time-separable frequencies without bandpass filtering or prior selection of wavelet features. Finally, we discuss in detail two of the extracted components, Beta (\( \sim \) 13 Hz) and high Gamma (\( \sim \) 50 Hz) frequencies, and explore the spatiotemporal dynamics of high- and low- frequency components.

Keywords

Koopman operator Spectral decomposition Nonlinear Spatiotemporal dynamics ECoG Brain Mismatch negativity 

Mathematics Subject Classification (2010)

37M10 37M25 58C40 30C40 37N25 47A35 92C55 

Notes

Acknowledgements

The authors would like to thank Ian Stevenson, Stephen Herzog, and the journal editorial team and reviewers for their helpful comments. Misako Komatsu, Kana Takura, and Naotaka Fujii from the RIKEN Brain Science Institute generously provided the open-access data used in this article. HLR has ownership interest in Elemind Technologies, Inc.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Psychological SciencesUniversity of ConnecticutStorrsUSA
  2. 2.Department of PhysicsUniversity of Wisconsin-MilwaukeeMilwaukeeUSA
  3. 3.Courant Institute of Mathematical SciencesNew York UniversityNew YorkUSA
  4. 4.Department of Psychological Sciences/Department of Biomedical EngineeringUniversity of ConnecticutStorrsUSA

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