Regression convolutional neural network for improved simultaneous EMG control

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dc.contributor.authorAmeri, Ali
dc.contributor.authorAli Akhaee, Mohammad
dc.contributor.authorScheme, Erik
dc.contributor.authorEnglehart, Kevin
dc.date.accessioned2023-06-28T13:07:26Z
dc.date.available2023-06-28T13:07:26Z
dc.date.issued2019
dc.description.abstractObjective. Deep learning models can learn representations of data that extract useful information in order to perform prediction without feature engineering. In this paper, an electromyography (EMG) control scheme with a regression convolutional neural network (CNN) is proposed as a substitute of conventional regression models that use purposefully designed features. Approach. The usability of the regression CNN model is validated for the first time, using an online Fitts' law style test with both individual and simultaneous wrist motions. Results were compared to that of a support vector regression-based scheme with a group of widely used extracted features. Main results. In spite of the proven efficiency of these well-known features, the CNN-based system outperformed the support vector machine (SVM) based scheme in throughput, due to higher regression accuracies especially with high EMG amplitudes. Significance. These results indicate that the CNN model can extract underlying motor control information from EMG signals during single and multiple degree-of-freedom (DoF) tasks. The advantage of regression CNN over classification CNN (studied previously) is that it allows independent and simultaneous control of motions.
dc.description.copyrightThis is the version of the article before peer review or editing, as submitted by an author to the Journal of Neural Engineering.  IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it.  The Version of Record is available online at https://doi.org/10.1088/1741-2552/ab0e2e
dc.identifier.doi10.1088/1741-2552/ab0e2e
dc.identifier.issn1741-2560
dc.identifier.issn1741-2552
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/37234
dc.language.isoen
dc.publisherIOP Publishing
dc.relation.hasversionhttps://doi.org/10.1088/1741-2552/ab0e2e
dc.relation.ispartofJournal of Neural Engineering
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineElectrical and Computer Engineering
dc.titleRegression convolutional neural network for improved simultaneous EMG control
dc.typejournal-article
oaire.citation.issue3
oaire.citation.titleJournal of Neural Engineering
oaire.citation.volume16
oaire.license.conditionother
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa

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