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

Yuan Yang; Alfred C. Schouten; Teodoro Solis-Escalante; Frans C T van der Helm

doi:10.1016/j.ifacol.2015.12.326

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

Yuan Yang, Alfred C. Schouten, Teodoro Solis-Escalante, Frans C T van der Helm

Biomechatronics & Human-Machine Control

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

5 Citations (Scopus)

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.

Original language	English
Title of host publication	IFAC-PapersOnline
Editors	Y Zhao
Publisher	Elsevier
Pages	1386-1390
Volume	48 - 28
DOIs	https://doi.org/10.1016/j.ifacol.2015.12.326
Publication status	Published - 2015
Event	17th IFAC Symposium on System Identification - Beijing, China Duration: 19 Oct 2015 → 21 Oct 2015

Conference

Conference	17th IFAC Symposium on System Identification
Abbreviated title	SYSID 2015
Country	China
City	Beijing
Period	19/10/15 → 21/10/15

Keywords

Biological Systems
Frequency Domain Identification
Nonlinear System Identification

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10.1016/j.ifacol.2015.12.326

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title = "Probing the nonlinearity in neural systems using cross-frequency coherence framework",

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.",

keywords = "Biological Systems, Frequency Domain Identification, Nonlinear System Identification",

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Probing the nonlinearity in neural systems using cross-frequency coherence framework. / Yang, Yuan; Schouten, Alfred C.; Solis-Escalante, Teodoro; van der Helm, Frans C T.

IFAC-PapersOnline. ed. / Y Zhao. Vol. 48 - 28 Elsevier, 2015. p. 1386-1390.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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