Cortico-muscular coherence enhancement via sparse signal representation

Yuhang Xu; Qi Yu; Wei Dai; Zoran Cvetkovic; Verity Maria McClelland

Cortico-muscular coherence enhancement via sparse signal representation

Yuhang Xu, Qi Yu, Wei Dai, Zoran Cvetkovic, Verity Maria McClelland

Imperial College London

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

2 Citations (Scopus)

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Abstract

Identifiction of specific cortico-muscular interactions is es- sential for understanding sensorimotor control. These inter- actions are commonly studied by analyzing cortico-muscular coherence (CMC) between electroencephalogram (EEG) and surface electromyogram (sEMG) recorded synchronously un- der a motor control task. However, the presence of noise and components irrelevant to the monitored task weakens CMC so that it is often very difficult to detect. This study proposes an approach based on dictionary learning and sparse signal rep- resentation combined with a component selection algorithm to extract versions of EEG and sEMG signals which contain higher relative levels of coherent components. Evaluations using neurophysiological data show that the method achieves substantial increase in CMC levels.

Original language	English
Title of host publication	IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Pages	1
Number of pages	5
Publication status	Published - Apr 2018

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title = "Cortico-muscular coherence enhancement via sparse signal representation",

abstract = "Identifiction of specific cortico-muscular interactions is es- sential for understanding sensorimotor control. These inter- actions are commonly studied by analyzing cortico-muscular coherence (CMC) between electroencephalogram (EEG) and surface electromyogram (sEMG) recorded synchronously un- der a motor control task. However, the presence of noise and components irrelevant to the monitored task weakens CMC so that it is often very difficult to detect. This study proposes an approach based on dictionary learning and sparse signal rep- resentation combined with a component selection algorithm to extract versions of EEG and sEMG signals which contain higher relative levels of coherent components. Evaluations using neurophysiological data show that the method achieves substantial increase in CMC levels.",

author = "Yuhang Xu and Qi Yu and Wei Dai and Zoran Cvetkovic and McClelland, {Verity Maria}",

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AU - Yu, Qi

AU - Dai, Wei

AU - Cvetkovic, Zoran

AU - McClelland, Verity Maria

PY - 2018/4

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N2 - Identifiction of specific cortico-muscular interactions is es- sential for understanding sensorimotor control. These inter- actions are commonly studied by analyzing cortico-muscular coherence (CMC) between electroencephalogram (EEG) and surface electromyogram (sEMG) recorded synchronously un- der a motor control task. However, the presence of noise and components irrelevant to the monitored task weakens CMC so that it is often very difficult to detect. This study proposes an approach based on dictionary learning and sparse signal rep- resentation combined with a component selection algorithm to extract versions of EEG and sEMG signals which contain higher relative levels of coherent components. Evaluations using neurophysiological data show that the method achieves substantial increase in CMC levels.

AB - Identifiction of specific cortico-muscular interactions is es- sential for understanding sensorimotor control. These inter- actions are commonly studied by analyzing cortico-muscular coherence (CMC) between electroencephalogram (EEG) and surface electromyogram (sEMG) recorded synchronously un- der a motor control task. However, the presence of noise and components irrelevant to the monitored task weakens CMC so that it is often very difficult to detect. This study proposes an approach based on dictionary learning and sparse signal rep- resentation combined with a component selection algorithm to extract versions of EEG and sEMG signals which contain higher relative levels of coherent components. Evaluations using neurophysiological data show that the method achieves substantial increase in CMC levels.

M3 - Conference paper

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BT - IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018

ER -

Cortico-muscular coherence enhancement via sparse signal representation

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