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Báo cáo khoa hoc:" Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces | Micera et al. Journal of NeuroEngineering and Rehabilitation 2011 8 53 http www.jneuroengrehab.eom content 8 1 53 Iril JOURNAL OF NEUROENGINEERING NCR AND REHABILITATION RESEARCH Open Access Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces 1.2 t 84t 1 I 1 1 Silvestro Micera 1 t Paolo M Rossini Jacopo Rigosa Luca Citi Jacopo Carpaneto Stanisa Raspopovic Mario Tombini3 Christian Cipriani1 Giovanni Assenza3 Maria C Carrozza1 Klaus-Peter Hoffmann5 Ken Yoshida6 Xavier Navarro7 and Paolo Dario1 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 .