Data-driven spectral decomposition of ECoG signal from an auditory oddball experiment in a marmoset monkey: Implications for EEG data in humans

Natasza Marrouch, Heather L. Read, Joanna Slawinska, Dimitrios Giannakis

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

Abstract

This paper presents a data-driven method to extract spatiotemporal dynamics of mismatch negativity in a marmoset monkey. In this, we treat electrocorticographic (ECoG) data as observables of a skew-product dynamical system and extract the patterns of the neural dynamics from the point of view of the operator-theoretic formulation of ergodic theory. We successfully extract time-separable frequencies without bandpass filtering. Second, we examine in more detail the frequency band most commonly associated with MMN-beta-band activity (13-20 Hz) and proceed to cross-validate our results with those obtained by Komatsu, Takaura, and Fuji (2015). Having ensured the compatibility and statistical significance of the results, we then examine the spatiotemporal dynamics, and we find that MMN is in part driven by a synchronization in brain response following a deviation in the auditory stimuli.

Original languageEnglish (US)
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060146
DOIs
StatePublished - Oct 10 2018
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: Jul 8 2018Jul 13 2018

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2018-July

Other

Other2018 International Joint Conference on Neural Networks, IJCNN 2018
Country/TerritoryBrazil
CityRio de Janeiro
Period7/8/187/13/18

Keywords

  • Koopman operators
  • electrophysiological data
  • kernel methods
  • mismatch negativity
  • spatiotemporal patterns

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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