Wodlinger B, Downey J E, Tyler-Kabara E C, Schwartz A B, Boninger M L, Collinger J L
Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA. Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
J Neural Eng. 2015 Feb;12(1):016011. doi: 10.1088/1741-2560/12/1/016011. Epub 2014 Dec 16.
In a previous study we demonstrated continuous translation, orientation and one-dimensional grasping control of a prosthetic limb (seven degrees of freedom) by a human subject with tetraplegia using a brain-machine interface (BMI). The current study, in the same subject, immediately followed the previous work and expanded the scope of the control signal by also extracting hand-shape commands from the two 96-channel intracortical electrode arrays implanted in the subject's left motor cortex.
Four new control signals, dictating prosthetic hand shape, replaced the one-dimensional grasping in the previous study, allowing the subject to control the prosthetic limb with ten degrees of freedom (three-dimensional (3D) translation, 3D orientation, four-dimensional hand shaping) simultaneously.
Robust neural tuning to hand shaping was found, leading to ten-dimensional (10D) performance well above chance levels in all tests. Neural unit preferred directions were broadly distributed through the 10D space, with the majority of units significantly tuned to all ten dimensions, instead of being restricted to isolated domains (e.g. translation, orientation or hand shape). The addition of hand shaping emphasized object-interaction behavior. A fundamental component of BMIs is the calibration used to associate neural activity to intended movement. We found that the presence of an object during calibration enhanced successful shaping of the prosthetic hand as it closed around the object during grasping.
Our results show that individual motor cortical neurons encode many parameters of movement, that object interaction is an important factor when extracting these signals, and that high-dimensional operation of prosthetic devices can be achieved with simple decoding algorithms. ClinicalTrials.gov Identifier: NCT01364480.
在之前的一项研究中，我们展示了一名四肢瘫痪的人类受试者通过脑机接口（BMI）对假肢（七个自由度）进行连续平移、定向和一维抓握控制。本研究在同一受试者身上紧接之前的工作开展，通过从植入受试者左运动皮层的两个96通道皮层内电极阵列中提取手形指令，扩大了控制信号的范围。
四个指示假肢手形的新控制信号取代了之前研究中的一维抓握控制，使受试者能够同时控制具有十个自由度的假肢（三维（3D）平移、3D定向、四维手形）。
发现了对手形塑造的稳健神经调谐，在所有测试中均实现了远超随机水平的十维（10D）性能。神经单元的偏好方向广泛分布于10D空间中，大多数单元对所有十个维度均有显著调谐，而非局限于孤立的领域（如平移、定向或手形）。手形塑造的加入突出了物体交互行为。BMI的一个基本组成部分是用于将神经活动与预期运动相关联的校准。我们发现，在校准过程中存在物体可增强假肢手在抓握时围绕物体闭合的成功塑形。
我们的结果表明，单个运动皮层神经元编码运动的多个参数，物体交互是提取这些信号时的一个重要因素，并且使用简单的解码算法即可实现假肢装置的高维操作。临床试验.gov标识符：NCT01364480。
