Investigation of Optimal Afferent Feedback Modality for Inducing Neural Plasticity With a Self-paced Brain-computer Interface

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aut.relation.issue11en_NZ
aut.relation.journalSensors (Switzerland)en_NZ
aut.relation.volume18en_NZ
dark.contributor.authorJochumsen, Men_NZ
dark.contributor.authorCremoux, Sen_NZ
dark.contributor.authorRobinault, Len_NZ
dark.contributor.authorLauber, Jen_NZ
dark.contributor.authorArceo, JCen_NZ
dark.contributor.authorNavid, MSen_NZ
dark.contributor.authorNedergaard, RWen_NZ
dark.contributor.authorRashid, Uen_NZ
dark.contributor.authorHaavik, Hen_NZ
dark.contributor.authorNiazi, IKen_NZ
dc.date.accessioned2020-05-19T03:14:24Z
dc.date.available2020-05-19T03:14:24Z
dc.date.copyright2018en_NZ
dc.date.issued2018en_NZ
dc.description.abstractBrain-computer interfaces (BCIs) can be used to induce neural plasticity in the human nervous system by pairing motor cortical activity with relevant afferent feedback, which can be used in neurorehabilitation. The aim of this study was to identify the optimal type or combination of afferent feedback modalities to increase cortical excitability in a BCI training intervention. In three experimental sessions, 12 healthy participants imagined a dorsiflexion that was decoded by a BCI which activated relevant afferent feedback: (1) electrical nerve stimulation (ES) (peroneal nerve—innervating tibialis anterior), (2) passive movement (PM) of the ankle joint, or (3) combined electrical stimulation and passive movement (Comb). The cortical excitability was assessed with transcranial magnetic stimulation determining motor evoked potentials (MEPs) in tibialis anterior before, immediately after and 30 min after the BCI training. Linear mixed regression models were used to assess the changes in MEPs. The three interventions led to a significant (p < 0.05) increase in MEP amplitudes immediately and 30 min after the training. The effect sizes of Comb paradigm were larger than ES and PM, although, these differences were not statistically significant (p > 0.05). These results indicate that the timing of movement imagery and afferent feedback is the main determinant of induced cortical plasticity whereas the specific type of feedback has a moderate impact. These findings can be important for the translation of such a BCI protocol to the clinical practice where by combining the BCI with the already available equipment cortical plasticity can be effectively induced. The findings in the current study need to be validated in stroke populations.en_NZ
dc.identifier.citationSensors, 18(11), 3761. doi:10.3390/s18113761
dc.identifier.doi10.3390/s18113761en_NZ
dc.identifier.issn1424-8220en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/13346
dc.publisherMDPI
dc.relation.urihttps://www.mdpi.com/1424-8220/18/11/3761
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectBrain-computer interface; Neural plasticity; Peripheral nerve stimulation; Exoskeleton; Neurorehabilitation; Transcranial magnetic stimulation (TMS)
dc.titleInvestigation of Optimal Afferent Feedback Modality for Inducing Neural Plasticity With a Self-paced Brain-computer Interfaceen_NZ
dc.typeJournal Article
pubs.elements-id349038
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Health & Environmental Science
pubs.organisational-data/AUT/Health & Environmental Science/Clinical Sciences
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