Vujaklija, Ivan

[["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.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"]]