Delay estimation between EEG and EMG via coherence with time lag

Yuhang Xu, Verity McClelland , Zoran Cvetkovic, Kerry Mills

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

2 Citations (Scopus)

Abstract

The traditional way to estimate the time delay between the motor cortex and the periphery is based on the estimation of the slope of the phase of the cross spectral density between motor cortex electroencephalogram (EEG) and electromyography (EMG) signals recorded synchronously during a motor control task. There are several issues that could make the delay estimation using this method subject to errors, leading frequently to estimates which are in disagreement with underlying physiology. This study introduces cortico-muscular coherence with time lag (CMCTL) function and proposes a method for estimating the delay based on finding its local maxima. We further address the issue of the interpretation of such time delay in multi-path propagation systems. Delay estimates obtained using the proposed method are more consistent compared with results obtained using the phase method and in a better agreement with physiological facts.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
ISBN (Electronic) 978-1-4799-9988-0
DOIs
Publication statusPublished - 19 May 2016
Externally publishedYes
Event International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Conference

Conference International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Abbreviated titleICASSP
CountryChina
CityShanghai
Period20/03/1625/03/16

Keywords

  • Cortico-muscular coherence
  • EEG
  • EMG
  • time delay

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  • Cite this

    Xu, Y., McClelland , V., Cvetkovic, Z., & Mills, K. (2016). Delay estimation between EEG and EMG via coherence with time lag. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) IEEE. https://doi.org/10.1109/ICASSP.2016.7471772