Negro, Francesco

[["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.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","2403"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","The Journal of Physiology"],["dc.bibliographiccitation.lastpage","2418"],["dc.bibliographiccitation.volume","591"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Negro, Francesco"],["dc.contributor.author","Jiang, Ning"],["dc.date.accessioned","2018-11-07T09:25:07Z"],["dc.date.available","2018-11-07T09:25:07Z"],["dc.date.issued","2013"],["dc.description.abstract","Key points center dot Oscillatory drives at the input of a pool of motor neurons are transmitted to the motor neuron output (neural drive to the muscle) in an approximately linear way if these inputs are common to all motor neurons. center dot The neural drive to the muscle is transformed in the EMG signal that can be thus used to extract information on the oscillatory inputs to motor neurons. center dot The transmission of oscillatory inputs is closer to a linear transmission for the raw or for the rectified EMG depending on the energy of the raw and rectified motor unit action potentials at the input frequency and on the degree of amplitude cancellation. center dot Amplitude cancellation negatively influences the effectiveness of EMG rectification in identifying with linear methods oscillatory inputs to motor neurons, so that rectification is preferable over the raw EMG only when the degree of cancellation is low. Abstract Oscillatory common inputs of cortical or peripheral origin can be identified from the motor neuron output with coherence analysis. Linear transmission is possible despite the motor neuron non-linearity because the same input is sent commonly to several neurons. Because of the linear transmission, common input components to motor neurons can be investigated from the surface EMG, for example by EEGEMG or EMGEMG coherence. In these studies, there is an open debate on the utility and appropriateness of EMG rectification. The present study addresses this issue using an analytical, simulation and experimental approach. The main novel theoretical contribution that we report is that the spectra of both the rectified and the raw EMG contain input spectral components to motor neurons. However, they differ by the contribution of amplitude cancellation which influences the rectified EMG spectrum when extracting common oscillatory inputs. Therefore, the degree of amplitude cancellation has an impact on the effectiveness of EMG rectification in extracting input spectral peaks. The theoretical predictions were exactly confirmed by realistic simulations of a pool of motor neurons innervating a muscle in a cylindrical volume conductor of EMG generation and by experiments conducted on the first dorsal interosseous and the abductor pollicis brevis muscles of seven healthy subjects during pinching. It was concluded that when the contraction level is relatively low, EMG rectification may be preferable for identifying common inputs to motor neurons, especially when the energy of the action potentials in the low frequency range is low. Nonetheless, different levels of cancellation across conditions influence the relative estimates of the degree of linear transmission of oscillatory inputs to motor neurons when using the rectified EMG."],["dc.identifier.doi","10.1113/jphysiol.2012.246082"],["dc.identifier.isi","000319016200007"],["dc.identifier.pmid","23507877"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/29990"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","0022-3751"],["dc.title","Identification of common synaptic inputs to motor neurons from the rectified electromyogram"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","251"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","The Journal of Physiology"],["dc.bibliographiccitation.lastpage","252"],["dc.bibliographiccitation.volume","592"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Negro, Francesco"],["dc.contributor.author","Jiang, Ning"],["dc.date.accessioned","2018-11-07T09:47:06Z"],["dc.date.available","2018-11-07T09:47:06Z"],["dc.date.issued","2014"],["dc.identifier.doi","10.1113/jphysiol.2013.267070"],["dc.identifier.isi","000329141200017"],["dc.identifier.pmid","24382922"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35033"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","1469-7793"],["dc.relation.issn","0022-3751"],["dc.title","Reply"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.subtype","letter_note"],["dspace.entity.type","Publication"]]