Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces

Journal of NeuroEngineering and Rehabilitation. 2011;8(1):53 DOI 10.1186/1743-0003-8-53

 

Journal Homepage

Journal Title: Journal of NeuroEngineering and Rehabilitation

ISSN: 1743-0003 (Online)

Publisher: BMC

LCC Subject Category: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB

 

AUTHORS

Cipriani Christian
Tombini Mario
Raspopovic Stanisa
Carpaneto Jacopo
Citi Luca
Rigosa Jacopo
Rossini Paolo M
Micera Silvestro
Assenza Giovanni
Carrozza Maria C
Hoffmann Klaus-Peter
Yoshida Ken
Navarro Xavier
Dario Paolo

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 32 weeks

 

Abstract | Full Text

<p>Abstract</p> <p>Background</p> <p>The restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting.</p> <p>Methods</p> <p>Thin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an amputee's stump during a four-week trial. The possibility of decoding motor commands suitable to control a dexterous hand prosthesis was investigated for the first time in this research field by implementing a spike sorting and classification algorithm.</p> <p>Results</p> <p>The results showed that motor information (e.g., grip types and single finger movements) could be extracted with classification accuracy around 85% (for three classes plus rest) and that the user could improve his ability to govern motor commands over time as shown by the improved discrimination ability of our classification algorithm.</p> <p>Conclusions</p> <p>These results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis.</p>