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

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)


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 languageEnglish
Title of host publicationIFAC-PapersOnline
EditorsY Zhao
Volume48 - 28
Publication statusPublished - 2015
Event17th IFAC Symposium on System Identification - Beijing, China
Duration: 19 Oct 201521 Oct 2015


Conference17th IFAC Symposium on System Identification
Abbreviated titleSYSID 2015


  • Biological Systems
  • Frequency Domain Identification
  • Nonlinear System Identification

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