Vujaklija, Ivan

[["dc.bibliographiccitation.artnumber","47"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of NeuroEngineering and Rehabilitation"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Kapelner, Tamás"],["dc.contributor.author","Vujaklija, Ivan"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Negro, Francesco"],["dc.contributor.author","Aszmann, Oskar C."],["dc.contributor.author","Principe, Jose"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2019-07-09T11:51:40Z"],["dc.date.available","2019-07-09T11:51:40Z"],["dc.date.issued","2019"],["dc.description.abstract","BACKGROUND: Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG. METHODS: We recorded wrist kinematics and high-density surface EMG signals from six able-bodied individuals and one patient with limb deficiency while they performed movements of three degrees of freedom of the wrist at three different speeds. We compared the performance of linear regression to predict the observed individual wrist joint angles from, either traditional time domain features of the interference EMG or from motor unit discharge timings (which we termed neural features) obtained by EMG decomposition. In addition, we propose and test a simple model-based dimensionality reduction, based on the physiological notion that the discharge timings of motor units are partly correlated. RESULTS: The regression approach using neural features outperformed regression on classic global EMG features (average R2 for neural features 0.77 and 0.64, for able-bodied subjects and patients, respectively; for time-domain features 0.70 and 0.52). CONCLUSIONS: These results indicate that the use of neural information extracted from EMG decomposition can advance man-machine interfacing for prosthesis control."],["dc.identifier.doi","10.1186/s12984-019-0516-x"],["dc.identifier.pmid","30953528"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16168"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59987"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/267888/EU//DEMOVE"],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/737570/EU//INTERSPINE"],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/702491/EU//NeuralCon"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.title","Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1333"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Systems and Rehabilitation Engineering"],["dc.bibliographiccitation.lastpage","1341"],["dc.bibliographiccitation.volume","24"],["dc.contributor.author","Hofmann, David"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Vujaklija, Ivan"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T10:05:01Z"],["dc.date.available","2018-11-07T10:05:01Z"],["dc.date.issued","2016"],["dc.description.abstract","The amplitude of the surface EMG (sEMG) is commonly estimated by rectification or other nonlinear transformations, followed by smoothing (low-pass linear filtering). Although computationally efficient, this approach leads to an estimation accuracy with a limited theoretical signal-to-noise ratio (SNR). Since sEMG amplitude is one of the most relevant features for myoelectric control, its estimate has become one of the limiting factors for the performance of myoelectric control applications, such as powered prostheses. In this study, we present a recursive nonlinear estimator of sEMG amplitude based on Bayesian filtering. Furthermore, we validate the advantage of the proposed Bayesian filter over the conventional linear filters through an online simultaneous and proportional control (SPC) task, performed by eight able-bodied subjects and three below-elbow limb deficient subjects. The results demonstrated that the proposed Bayesian filter provides significantly more accurate SPC, particularly for the patients, when compared with conventional linear filters. This result presents a major step toward accurate prosthetic control for advanced multi-function prostheses."],["dc.identifier.doi","10.1109/TNSRE.2015.2501979"],["dc.identifier.isi","000390559600007"],["dc.identifier.pmid","26600161"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14165"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/38814"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","1558-0210"],["dc.relation.issn","1534-4320"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Bayesian Filtering of Surface EMG for Accurate Simultaneous and Proportional Prosthetic Control"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0149772"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Kapelner, Tamas"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Holobar, Ales"],["dc.contributor.author","Vujaklija, Ivan"],["dc.contributor.author","Roche, Aidan Dominic"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Aszmann, Oskar C."],["dc.date.accessioned","2018-11-07T10:18:06Z"],["dc.date.available","2018-11-07T10:18:06Z"],["dc.date.issued","2016"],["dc.description.abstract","Targeted muscle reinnervation (TMR) is a surgical procedure used to redirect nerves originally controlling muscles of the amputated limb into remaining muscles above the amputation, to treat phantom limb pain and facilitate prosthetic control. While this procedure effectively establishes robust prosthetic control, there is little knowledge on the behavior and characteristics of the reinnervated motor units. In this study we compared the m. pectoralis of five TMR patients to nine able-bodied controls with respect to motor unit action potential (MUAP) characteristics. We recorded and decomposed high-density surface EMG signals into individual spike trains of motor unit action potentials. In the TMR patients the MUAP surface area normalized to the electrode grid surface (0.25 +/- 0.17 and 0.81 +/- 0.46, p < 0.001) and the MUAP duration (10.92 +/- 3.89 ms and 14.03 +/- 3.91 ms, p < 0.01) were smaller for the TMR group than for the controls. The mean MUAP amplitude (0.19 +/- 0.11 mV and 0.14 +/- 0.06 mV, p = 0.07) was not significantly different between the two groups. Finally, we observed that MUAP surface representation in TMR generally overlapped, and the surface occupied by motor units corresponding to only one motor task was on average smaller than 12% of the electrode surface. These results suggest that smaller MUAP surface areas in TMR patients do not necessarily facilitate prosthetic control due to a high degree of overlap between these areas, and a neural information-based control could lead to improved performance. Based on the results we also infer that the size of the motor units after reinnervation is influenced by the size of the innervating motor neuron."],["dc.identifier.doi","10.1371/journal.pone.0149772"],["dc.identifier.isi","000371276100148"],["dc.identifier.pmid","26901631"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12930"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/41360"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Motor Unit Characteristics after Targeted Muscle Reinnervation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]