Motor Unit-Driven Identification of Pathological Tremor in Electroencephalograms
Background: Traditional studies on the neural mechanisms of tremor use coherence analysis to investigate the relationship between cortical and muscle activity, measured by electroencephalograms (EEG) and electromyograms (EMG). This methodology is limited by the need of relatively long signal recordi...
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doaj-506532e49a994777b7f962b6a07fbea02020-11-25T02:32:24ZengFrontiers Media S.A.Frontiers in Neurology1664-22952018-10-01910.3389/fneur.2018.00879411178Motor Unit-Driven Identification of Pathological Tremor in ElectroencephalogramsAleš Holobar0Juan A. Gallego1Jernej Kranjec2Eduardo Rocon3Juan P. Romero4Juan P. Romero5Julián Benito-León6Julián Benito-León7Julián Benito-León8José L. Pons9Vojko Glaser10Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SloveniaNeural and Cognitive Engineering Group, Centre for Automation and Robotics, Spanish National Research Council, Arganda del Rey, SpainFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SloveniaNeural and Cognitive Engineering Group, Centre for Automation and Robotics, Spanish National Research Council, Arganda del Rey, SpainNeurorehabilitation and Brain Damage Research Group, Experimental Sciences School, Universidad Francisco de Vitoria, Madrid, SpainBrain Damage Unit, Hospital Beata María Ana, Madrid, SpainDepartment of Neurology, University Hospital 12 de Octubre, Madrid, SpainCenter of Biomedical Network Research on Neurodegenerative Diseases, Madrid, SpainDepartment of Medicine, Faculty of Medicine, Complutense University of Madrid, Madrid, SpainNeural Rehabilitation Group, Cajal Institute, Spanish National Research Council, Madrid, SpainFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SloveniaBackground: Traditional studies on the neural mechanisms of tremor use coherence analysis to investigate the relationship between cortical and muscle activity, measured by electroencephalograms (EEG) and electromyograms (EMG). This methodology is limited by the need of relatively long signal recordings, and it is sensitive to EEG artifacts. Here, we analytically derive and experimentally validate a new method for automatic extraction of the tremor-related EEG component in pathological tremor patients that aims to overcome these limitations.Methods: We exploit the coupling between the tremor-related cortical activity and motor unit population firings to build a linear minimum mean square error estimator of the tremor component in EEG. We estimated the motor unit population activity by decomposing surface EMG signals into constituent motor unit spike trains, which we summed up into a cumulative spike train (CST). We used this CST to initialize our tremor-related EEG component estimate, which we optimized using a novel approach proposed here.Results: Tests on simulated signals demonstrate that our new method is robust to both noise and motor unit firing variability, and that it performs well across a wide range of spectral characteristics of the tremor. Results on 9 essential (ET) and 9 Parkinson's disease (PD) patients show a ~2-fold increase in amplitude of the coherence between the estimated EEG component and the CST, compared to the classical EEG-EMG coherence analysis.Conclusions: We have developed a novel method that allows for more precise and robust estimation of the tremor-related EEG component. This method does not require artifact removal, provides reliable results in relatively short datasets, and tracks changes in the tremor-related cortical activity over time.https://www.frontiersin.org/article/10.3389/fneur.2018.00879/fullpathological tremorEEG decompositionsurface EMG decompositionParkinsonian tremoressential tremor |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Aleš Holobar Juan A. Gallego Jernej Kranjec Eduardo Rocon Juan P. Romero Juan P. Romero Julián Benito-León Julián Benito-León Julián Benito-León José L. Pons Vojko Glaser |
spellingShingle |
Aleš Holobar Juan A. Gallego Jernej Kranjec Eduardo Rocon Juan P. Romero Juan P. Romero Julián Benito-León Julián Benito-León Julián Benito-León José L. Pons Vojko Glaser Motor Unit-Driven Identification of Pathological Tremor in Electroencephalograms Frontiers in Neurology pathological tremor EEG decomposition surface EMG decomposition Parkinsonian tremor essential tremor |
author_facet |
Aleš Holobar Juan A. Gallego Jernej Kranjec Eduardo Rocon Juan P. Romero Juan P. Romero Julián Benito-León Julián Benito-León Julián Benito-León José L. Pons Vojko Glaser |
author_sort |
Aleš Holobar |
title |
Motor Unit-Driven Identification of Pathological Tremor in Electroencephalograms |
title_short |
Motor Unit-Driven Identification of Pathological Tremor in Electroencephalograms |
title_full |
Motor Unit-Driven Identification of Pathological Tremor in Electroencephalograms |
title_fullStr |
Motor Unit-Driven Identification of Pathological Tremor in Electroencephalograms |
title_full_unstemmed |
Motor Unit-Driven Identification of Pathological Tremor in Electroencephalograms |
title_sort |
motor unit-driven identification of pathological tremor in electroencephalograms |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neurology |
issn |
1664-2295 |
publishDate |
2018-10-01 |
description |
Background: Traditional studies on the neural mechanisms of tremor use coherence analysis to investigate the relationship between cortical and muscle activity, measured by electroencephalograms (EEG) and electromyograms (EMG). This methodology is limited by the need of relatively long signal recordings, and it is sensitive to EEG artifacts. Here, we analytically derive and experimentally validate a new method for automatic extraction of the tremor-related EEG component in pathological tremor patients that aims to overcome these limitations.Methods: We exploit the coupling between the tremor-related cortical activity and motor unit population firings to build a linear minimum mean square error estimator of the tremor component in EEG. We estimated the motor unit population activity by decomposing surface EMG signals into constituent motor unit spike trains, which we summed up into a cumulative spike train (CST). We used this CST to initialize our tremor-related EEG component estimate, which we optimized using a novel approach proposed here.Results: Tests on simulated signals demonstrate that our new method is robust to both noise and motor unit firing variability, and that it performs well across a wide range of spectral characteristics of the tremor. Results on 9 essential (ET) and 9 Parkinson's disease (PD) patients show a ~2-fold increase in amplitude of the coherence between the estimated EEG component and the CST, compared to the classical EEG-EMG coherence analysis.Conclusions: We have developed a novel method that allows for more precise and robust estimation of the tremor-related EEG component. This method does not require artifact removal, provides reliable results in relatively short datasets, and tracks changes in the tremor-related cortical activity over time. |
topic |
pathological tremor EEG decomposition surface EMG decomposition Parkinsonian tremor essential tremor |
url |
https://www.frontiersin.org/article/10.3389/fneur.2018.00879/full |
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