Nonlinear System Identification of Neural Systems from Neurophysiological Signals

Fei He, Yuan Yang

Research output: Contribution to journalReview articlepeer-review

Abstract

The human nervous system is one of the most complicated systems in nature. Complex nonlinear behaviours have been shown from the single neuron level to the system level. For decades, linear connectivity analysis methods, such as correlation, coherence and Granger causality, have been extensively used to assess the neural connectivities and input–output interconnections in neural systems. Recent studies indicate that these linear methods can only capture a certain amount of neural activities and functional relationships, and therefore cannot describe neural behaviours in a precise or complete way. In this review, we highlight recent advances in nonlinear system identification of neural systems, corresponding time and frequency domain analysis, and novel neural connectivity measures based on nonlinear system identification techniques. We argue that nonlinear modelling and analysis are necessary to study neuronal processing and signal transfer in neural systems quantitatively. These approaches can hopefully provide new insights to advance our understanding of neurophysiological mechanisms underlying neural functions. These nonlinear approaches also have the potential to produce sensitive biomarkers to facilitate the development of precision diagnostic tools for evaluating neurological disorders and the effects of targeted intervention.

Original languageEnglish
Pages (from-to)213-228
Number of pages16
JournalNeuroscience
Volume458
Early online date11 Dec 2020
DOIs
Publication statusE-pub ahead of print - 11 Dec 2020

Bibliographical note

Copyright © 2020. Published by Elsevier Ltd.

Funder

NIH NICHD R21HD099710

Keywords

  • Causality analysis
  • Computational neuroscience
  • EEG
  • Functional connectivity
  • Nonlinear system identification
  • Signal processing

ASJC Scopus subject areas

  • Neuroscience(all)

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