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: Contribution to journalArticlepeer-review


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 (US)
Pages (from-to)1386-1390
Number of pages5
Issue number28
StatePublished - 2015
Externally publishedYes


  • Biological Systems
  • Frequency Domain Identification
  • Nonlinear System Identification

ASJC Scopus subject areas

  • Control and Systems Engineering


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