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dc.contributor.author
Cracchiolo, Marina
dc.contributor.author
Panarese, Alessandro
dc.contributor.author
Valle, Giacomo
dc.contributor.author
Strauss, Ivo
dc.contributor.author
Granata, Giuseppe
dc.contributor.author
Di Iorio, Riccardo
dc.contributor.author
Stieglitz, Thomas
dc.contributor.author
Rossini, Paolo M.
dc.contributor.author
Mazzoni, Alberto
dc.contributor.author
Micera, Silvestro
dc.date.accessioned
2021-04-27T11:44:56Z
dc.date.available
2021-04-24T03:11:24Z
dc.date.available
2021-04-27T11:44:56Z
dc.date.issued
2021-10
dc.identifier.issn
1741-2560
dc.identifier.issn
1741-2552
dc.identifier.other
10.1088/1741-2552/abef3a
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/480346
dc.description.abstract
Objective. Recent results have shown the potentials of neural interfaces to provide sensory feedback to subjects with limb amputation increasing prosthesis usability. However, their advantages for decoding motor control signals over current methods based on electromyography (EMG) are still debated. In this study we compared a standard EMG-based method with approaches that use peripheral intraneural data to infer distinct levels of grasping force and velocity in a trans-radial amputee. Approach. Surface EMG (three channels) and intraneural signals (collected with transverse intrafascicular multichannel electrodes, TIMEs, 56 channels) were simultaneously recorded during the amputee's intended grasping movements. We sorted single unit activity (SUA) from each neural signal and then we identified the most informative units. EMG envelopes were extracted from the recorded EMG signals. A reference support vector machine (SVM) classifier was used to map EMG envelopes into desired force and velocity levels. Two decoding approaches using SUA were then tested and compared to the EMG-based reference classifier: (a) SVM classification of firing rates into desired force and velocity levels; (b) reconstruction of covariates (the grasp cue level or EMG envelopes) from neural data and use of covariates for classification into desired force and velocity levels. Main results. Using EMG envelopes as reconstructed covariates from SUA yielded significantly better results than the other approaches tested, with performance similar to that of the EMG-based reference classifier, and stable over three different recording days. Of the two reconstruction algorithms used in this approach, a linear Kalman filter and a nonlinear point process adaptive filter, the nonlinear filter gave better results. Significance. This study presented a new effective approach for decoding grasping force and velocity from peripheral intraneural signals in a trans-radial amputee, which relies on using SUA to reconstruct EMG envelopes. Being dependent on EMG recordings only for the training phase, this approach can fully exploit the advantages of implanted neural interfaces and potentially overcome, in the medium to long term, current state-of-the-art methods. (Clinical trial's registration number: NCT02848846).
en_US
dc.language.iso
en
en_US
dc.publisher
IOP Publishing
dc.subject
neuroprosthesis
en_US
dc.subject
neural decoding
en_US
dc.subject
dimensionality reduction
en_US
dc.subject
peripheral interface
en_US
dc.title
Computational approaches to decode grasping force and velocity level in upper-limb amputee from intraneural peripheral signals
en_US
dc.type
Journal Article
dc.date.published
2021-04-06
ethz.journal.title
Journal of Neural Engineering
ethz.journal.volume
18
en_US
ethz.journal.issue
5
en_US
ethz.pages.start
055001
en_US
ethz.size
17 p.
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Bristol
ethz.publication.status
published
en_US
ethz.date.deposited
2021-04-24T03:11:28Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-04-27T11:45:07Z
ethz.rosetta.lastUpdated
2024-02-02T13:34:43Z
ethz.rosetta.versionExported
true
ethz.COinS
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