Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces
Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces
复制标题
DOI：
10.1186/1743-0003-8-53
复制
发表时间：
2011-09-05
影响因子：
5.1
通讯作者：
Dario, Paolo
中科院分区：
文献类型：
--
作者：
Micera, Silvestro;Rossini, Paolo M.;Dario, Paolo
背景资料：通过在神经系统和灵巧手假体之间建立一种新的双向联系来恢复复杂的手功能，目前有几个研究小组在研究。这种连接必须快速、直观、成功率高并且非常自然，以允许用户的神经系统和智能人工设备之间的有效双向信息流。这一目标可以实现与几种方法，其中，使用与周围神经系统，即内神经束电极连接的植入式接口，被认为是特别interesting.Methods：薄膜纵向内神经束电极被植入在正中神经和尺神经的截肢者的残端在为期四周的试验。在本研究领域首次通过实现尖峰排序和分类算法研究了解码适合于控制灵巧手假肢的运动命令的可能性。例如，在一个实施例中，抓地力类型和单指运动）可以提取与分类准确率约85%（三个类加休息），用户可以提高他的能力，随着时间的推移，我们的分类algorithm.Conclusions的改进的歧视能力所示的电机命令：这些结果开辟了新的和有前途的可能性，神经控制的手假体的发展。
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.