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

Micera, S and Rossini, PM and Rigosa, J and Citi, L and Carpaneto, J and Raspopovic, S and Tombini, M and Cipriani, C and Assenza, G and Carrozza, MC and Hoffmann, KP and Yoshida, K and Navarro, X and Dario, P (2011) 'Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces.' Journal of NeuroEngineering and Rehabilitation, 8 (1). ISSN 1743-0003

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Abstract

Background: 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. Methods. 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. Results: 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. Conclusions: These results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis. © 2011 Micera et al; licensee BioMed Central Ltd.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 13 Mar 2014 14:16
Last Modified: 05 Feb 2019 19:15
URI: http://repository.essex.ac.uk/id/eprint/8786

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