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

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

Journal of NeuroEngineering and Rehabilitation, 01 September 2011, Vol.8(1), p.53 [Tạp chí có phản biện]

ISSN: 1743-0003 ; E-ISSN: 1743-0003 ; DOI: 10.1186/1743-0003-8-53

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  • Nhan đề:
    Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces
  • Tác giả: 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
  • Chủ đề: Medicine ; Engineering ; Occupational Therapy & Rehabilitation ; Physical Therapy
  • Là 1 phần của: Journal of NeuroEngineering and Rehabilitation, 01 September 2011, Vol.8(1), p.53
  • Mô tả: 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.
  • Ngôn ngữ: English
  • Số nhận dạng: ISSN: 1743-0003 ; E-ISSN: 1743-0003 ; DOI: 10.1186/1743-0003-8-53

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