Probing the nonlinearity in neural systems using cross-frequency coherence framework

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Probing the nonlinearity in neural systems using cross-frequency coherence framework

Conference Paper (2015)

Author(s)

Yuan Yang (TU Delft - Mechanical Engineering)

Alfred C. Schouten (TU Delft - Mechanical Engineering, University of Twente)

Teodoro Solis-Escalante (TU Delft - Mechanical Engineering)

Frans C T van der Helm (TU Delft - Mechanical Engineering)

Research Group

Biomechatronics & Human-Machine Control

Biological Systems Frequency Domain Identification Nonlinear System Identification

DOI related publication

https://doi.org/10.1016/j.ifacol.2015.12.326 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:5178b13f-559f-4f09-b96e-4fcaa1545c92

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Publication Year

2015

Language

English

Research Group

Biomechatronics & Human-Machine Control

Volume number

48 - 28

Pages (from-to)

1386-1390

Publisher

Elsevier

Event

17th IFAC Symposium on System Identification (2015-10-19 - 2015-10-21), Beijing, China

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Abstract

Neural systems can present various types of nonlinear input-output relationships, such as harmonic, subharmonic, and/or intermodulation coupling. This paper aims to introduce a general framework in frequency domain for detecting and characterizing nonlinear coupling in neural systems, called the cross-frequency coherence framework (CFCF). CFCF is an extension of classic coherence based on higher-order statistics. We demonstrate an application of CFCF for identifying nonlinear interactions in human motion control. Our results indicate that CFCF can effectively characterize nonlinear properties of the afferent sensory pathway. We conclude that CFCF contributes to identifying nonlinear transfer in neural systems.