Morel, Pierre

[["dc.bibliographiccitation.artnumber","016002"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of Neural Engineering"],["dc.bibliographiccitation.lastpage","15"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Morel, Pierre"],["dc.contributor.author","Ferrea, Enrico"],["dc.contributor.author","Taghizadeh-Sarshouri, Bahareh"],["dc.contributor.author","Audí, Josep Marcel Cardona"],["dc.contributor.author","Ruff, Roman"],["dc.contributor.author","Hoffmann, Klaus-Peter"],["dc.contributor.author","Lewis, Sören"],["dc.contributor.author","Russold, Michael"],["dc.contributor.author","Dietl, Hans"],["dc.contributor.author","Abu-Saleh, Lait"],["dc.contributor.author","Schroeder, Dietmar"],["dc.contributor.author","Krautschneider, Wolfgang"],["dc.contributor.author","Meiners, Thomas"],["dc.contributor.author","Gail, Alexander"],["dc.date.accessioned","2017-09-07T11:47:44Z"],["dc.date.available","2017-09-07T11:47:44Z"],["dc.date.issued","2015"],["dc.description.abstract","OBJECTIVE:The ease of use and number of degrees of freedom of current myoelectric hand prostheses is limited by the information content and reliability of the surface electromyography (sEMG) signals used to control them. For example, cross-talk limits the capacity to pick up signals from small or deep muscles, such as the forearm muscles for distal arm amputations, or sites of targeted muscle reinnervation (TMR) for proximal amputations. Here we test if signals recorded from the fully implanted, induction-powered wireless Myoplant system allow long-term decoding of continuous as well as discrete movement parameters with better reliability than equivalent sEMG recordings. The Myoplant system uses a centralized implant to transmit broadband EMG activity from four distributed bipolar epimysial electrodes.APPROACH:Two Rhesus macaques received implants in their backs, while electrodes were placed in their upper arm. One of the monkeys was trained to do a cursor task via a haptic robot, allowing us to control the forces exerted by the animal during arm movements. The second animal was trained to perform a center-out reaching task on a touchscreen. We compared the implanted system with concurrent sEMG recordings by evaluating our ability to decode time-varying force in one animal and discrete reach directions in the other from multiple features extracted from the raw EMG signals.MAIN RESULTS:In both cases, data from the implant allowed a decoder trained with data from a single day to maintain an accurate decoding performance during the following months, which was not the case for concurrent surface EMG recordings conducted simultaneously over the same muscles.SIGNIFICANCE:These results show that a fully implantable, centralized wireless EMG system is particularly suited for long-term stable decoding of dynamic movements in demanding applications such as advanced forelimb prosthetics in a wide range of configurations (distal amputations, TMR)."],["dc.identifier.doi","10.1088/1741-2560/13/1/016002"],["dc.identifier.gro","3150711"],["dc.identifier.pmid","26643959"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14153"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/7498"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.relation.issn","1741-2560"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Long-term decoding of movement force and direction with a wireless myoelectric implant"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Ferrea, E."],["dc.contributor.author","Franke, J."],["dc.contributor.author","Morel, P."],["dc.contributor.author","Gail, A."],["dc.date.accessioned","2022-07-01T07:34:52Z"],["dc.date.available","2022-07-01T07:34:52Z"],["dc.date.issued","2022"],["dc.description.abstract","Abstract Neurorehabilitation in patients suffering from motor deficits relies on relearning or re-adapting motor skills. Yet our understanding of motor learning is based mostly on results from one or two-dimensional experimental paradigms with highly confined movements. Since everyday movements are conducted in three-dimensional space, it is important to further our understanding about the effect that gravitational forces or perceptual anisotropy might or might not have on motor learning along all different dimensions relative to the body. Here we test how well existing concepts of motor learning generalize to movements in 3D. We ask how a subject’s variability in movement planning and sensory perception influences motor adaptation along three different body axes. To extract variability and relate it to adaptation rate, we employed a novel hierarchical two-state space model using Bayesian modeling via Hamiltonian Monte Carlo procedures. Our results show that differences in adaptation rate occur between the coronal, sagittal and horizontal planes and can be explained by the Kalman gain, i.e., a statistically optimal solution integrating planning and sensory information weighted by the inverse of their variability. This indicates that optimal integration theory for error correction holds for 3D movements and explains adaptation rate variation between movements in different planes."],["dc.description.sponsorship"," Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659"],["dc.description.sponsorship","Federal Ministry for Education and Research, Germany"],["dc.description.sponsorship","Deutsches Primatenzentrum GmbH - Leibniz-Institut für Primatenforschung"],["dc.identifier.doi","10.1038/s41598-022-13866-y"],["dc.identifier.pii","13866"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112030"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-581"],["dc.relation.eissn","2045-2322"],["dc.relation.haserratum","/handle/2/113626"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Statistical determinants of visuomotor adaptation along different dimensions during naturalistic 3D reaches"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Ferrea, E."],["dc.contributor.author","Franke, J."],["dc.contributor.author","Morel, P."],["dc.contributor.author","Gail, A."],["dc.date.accessioned","2022-09-01T09:50:07Z"],["dc.date.available","2022-09-01T09:50:07Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1038/s41598-022-16148-9"],["dc.identifier.pii","16148"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113626"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-597"],["dc.relation.eissn","2045-2322"],["dc.relation.iserratumof","/handle/2/112030"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Author Correction: Statistical determinants of visuomotor adaptation along different dimensions during naturalistic 3D reaches"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","erratum_ja"],["dspace.entity.type","Publication"]]