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

Conference paper (2015)

Authors

Y. Yang Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
A.C. Schouten University of Twente, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
T. Solis Escalante Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Frans van der Helm Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Research Group
Biomechatronics & Human-Machine Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2015

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Biomechanical Engineering

Research Group

Biomechatronics & Human-Machine Control

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.