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.firstpage","501"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Systems and Rehabilitation Engineering"],["dc.bibliographiccitation.lastpage","510"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Rehbaum, Hubertus"],["dc.contributor.author","Vujaklija, Ivan"],["dc.contributor.author","Graimann, Bernhard"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:40:24Z"],["dc.date.available","2018-11-07T09:40:24Z"],["dc.date.issued","2014"],["dc.description.abstract","We propose an approach for online simultaneous and proportional myoelectric control of two degrees-of-freedom (DoF) of the wrist, using surface electromyographic signals. The method is based on the nonnegativematrix factorization (NMF) of the wrist muscle activation to extract low-dimensional control signals translated by the user into kinematic variables. This procedure does not need a training set of signals for which the kinematics is known (labeled dataset) and is thus unsupervised (although it requires an initial calibration without labeled signals). The estimated control signals using NMF are used to directly control two DoFs of wrist. The method was tested on seven subjects with upper limb deficiency and on seven able-bodied subjects. The subjects performed online control of a virtual object with two DoFs to achieve goal-oriented tasks. The performance of the two subject groups, measured as the task completion rate, task completion time, and execution efficiency, was not statistically different. The approach was compared, and demonstrated to be superior to the online control by the industrial state-of-the-art approach. These results show that this new approach, which has several advantages over the previous myoelectric prosthetic control systems, has the potential of providing intuitive and dexterous control of artificial limbs for amputees."],["dc.identifier.doi","10.1109/TNSRE.2013.2278411"],["dc.identifier.isi","000342079300008"],["dc.identifier.pmid","23996582"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33499"],["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.title","Intuitive, Online, Simultaneous, and Proportional Myoelectric Control Over Two Degrees-of-Freedom in Upper Limb Amputees"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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"]]

[["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Nature Biomedical Engineering"],["dc.bibliographiccitation.volume","1"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Vujaklija, Ivan"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Kapelner, Tamás"],["dc.contributor.author","Negro, Francesco"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Bergmeister, Konstantin"],["dc.contributor.author","Andalib, Arash"],["dc.contributor.author","Principe, Jose"],["dc.contributor.author","Aszmann, Oskar C."],["dc.date.accessioned","2020-12-10T18:09:54Z"],["dc.date.available","2020-12-10T18:09:54Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1038/s41551-016-0025"],["dc.identifier.eissn","2157-846X"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73794"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","549"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Systems and Rehabilitation Engineering"],["dc.bibliographiccitation.lastpage","558"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Vujaklija, Ivan"],["dc.contributor.author","Rehbaum, Hubertus"],["dc.contributor.author","Graimann, Bernhard"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:40:24Z"],["dc.date.available","2018-11-07T09:40:24Z"],["dc.date.issued","2014"],["dc.description.abstract","In this paper, we present a systematic analysis of the relationship between the accuracy of the mapping between EMG and hand kinematics and the control performance in goal-oriented tasks of three simultaneous and proportional myoelectric control algorithms: nonnegative matrix factorization (NMF), linear regression (LR), and artificial neural networks (ANN). The purpose was to investigate the impact of the precision of the kinematics estimation by a myoelectric controller for accurately complete goal-directed tasks. Nine naive subjects performed a series of goal-directed myoelectric control tasks using the three algorithms, and their online performance was characterized by 6 indexes. The results showed that, although the three algorithms' mapping accuracies were significantly different, their online performance was similar. Moreover, for LR and ANN, the offline performance was not correlated to any of the online performance indexes, and only a weak correlation was found with three of them for NMF (r(2) < 50%). We conclude that for reliable simultaneous and proportional myoelectric control, it is not necessary to achieve high accuracy in the mapping between EMG and kinematics. Rather, good online myoelectric control is achieved by the continuous interaction and adaptation of the user with the myoelectric controller through feedback (visual in the current study). Control signals generated by EMG with rather poor association with kinematic variables can still be fully exploited by the user for precise control. This conclusion explains the possibility of accurate simultaneous and proportional control over multiple degrees of freedom when using unsupervised algorithms, such as NMF."],["dc.identifier.doi","10.1109/TNSRE.2013.2287383"],["dc.identifier.isi","000342079300013"],["dc.identifier.pmid","24235278"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33500"],["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.title","Is Accurate Mapping of EMG Signals on Kinematics Needed for Precise Online Myoelectric Control?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","810"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Systems and Rehabilitation Engineering"],["dc.bibliographiccitation.lastpage","819"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Rehbaum, Hubertus"],["dc.contributor.author","Holobar, Ales"],["dc.contributor.author","Vujaklija, Ivan"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Hofer, Christian"],["dc.contributor.author","Salminger, Stefan"],["dc.contributor.author","van Vliet, Hans-Willem"],["dc.contributor.author","Aszmann, Oskar C."],["dc.date.accessioned","2018-11-07T09:37:54Z"],["dc.date.available","2018-11-07T09:37:54Z"],["dc.date.issued","2014"],["dc.description.abstract","Targeted muscle reinnervation (TMR) redirects nerves that have lost their target, due to amputation, to remaining muscles in the region of the stump with the intent of establishing intuitive myosignals to control a complex prosthetic device. In order to directly recover the neural code underlying an attempted limb movement, in this paper, we present the decomposition of high-density surface electromyographic (EMG) signals detected from three TMR patients into the individual motor unit spike trains. The aim was to prove, for the first time, the feasibility of decoding the neural drive that would reach muscles of the missing limb in TMR patients, to show the accuracy of the decoding, and to demonstrate the representativeness of the pool of extracted motor units. Six to seven flexible EMG electrode grids of 64 electrodes each were mounted over the reinnervated muscles of each patient, resulting in up to 448 EMG signals. The subjects were asked to attempt elbow extension and flexion, hand open and close, wrist extension and flexion, wrist pronation and supination, of their missing limb. The EMG signals were decomposed using the Convolution Kernel Compensation technique and the decomposition accuracy was evaluated with a signal-based index of accuracy, called pulse-to-noise ratio (PNR). The results showed that the spike trains of 3 to 27 motor units could be identified for each task, with a sensitivity of the decomposition 90%, as revealed by PNR. The motor unit discharge rates were within physiological values of normally innervated muscles. Moreover, the detected motor units showed a high degree of common drive so that the set of extracted units per task was representative of the behavior of the population of active units. The results open a path for a new generation of human-machine interfaces in which the control signals are extracted from noninvasive recordings and the obtained neural information is based directly on the spike trains of motor neurons."],["dc.identifier.doi","10.1109/TNSRE.2014.2306000"],["dc.identifier.isi","000342080000011"],["dc.identifier.pmid","24760935"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/32949"],["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.title","Noninvasive, Accurate Assessment of the Behavior of Representative Populations of Motor Units in Targeted Reinnervated Muscles"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]