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 journalArticleAcademicpeer-review

    7 Citations (Scopus)
    26 Downloads (Pure)


    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
    Pages (from-to)1386-1390
    Number of pages5
    Issue number28
    Publication statusPublished - 2015


    • Biological Systems
    • Frequency Domain Identification
    • Nonlinear System Identification


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