Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106.
Department of Neurosurgery, Stanford University, Stanford, CA 94035.
eNeuro. 2021 Feb 19;8(1). doi: 10.1523/ENEURO.0231-20.2020. Print 2021 Jan-Feb.
Intracortical brain-computer interfaces (iBCIs) have the potential to restore hand grasping and object interaction to individuals with tetraplegia. Optimal grasping and object interaction require simultaneous production of both force and grasp outputs. However, since overlapping neural populations are modulated by both parameters, grasp type could affect how well forces are decoded from motor cortex in a closed-loop force iBCI. Therefore, this work quantified the neural representation and offline decoding performance of discrete hand grasps and force levels in two human participants with tetraplegia. Participants attempted to produce three discrete forces (light, medium, hard) using up to five hand grasp configurations. A two-way Welch ANOVA was implemented on multiunit neural features to assess their modulation to and Demixed principal component analysis (dPCA) was used to assess for population-level tuning to force and grasp and to predict these parameters from neural activity. Three major findings emerged from this work: (1) force information was neurally represented and could be decoded across multiple hand grasps (and, in one participant, across attempted elbow extension as well); (2) grasp type affected force representation within multiunit neural features and offline force classification accuracy; and (3) grasp was classified more accurately and had greater population-level representation than force. These findings suggest that force and grasp have both independent and interacting representations within cortex, and that incorporating force control into real-time iBCI systems is feasible across multiple hand grasps if the decoder also accounts for grasp type.
皮层内脑机接口（iBCI）有可能使四肢瘫痪患者恢复手部抓握和物体交互功能。最佳的抓握和物体交互需要同时产生力和抓握输出。然而，由于重叠的神经群体同时受到这两个参数的调节，因此在闭环力 iBCI 中，抓握类型可能会影响从运动皮层解码力的效果。因此，这项工作在两名四肢瘫痪的人类参与者中量化了离散手抓握和力水平的神经表示和离线解码性能。参与者试图使用多达五种手抓握配置来产生三种离散力（轻、中、重）。采用双向 Welch ANOVA 对多单位神经特征进行分析，以评估它们对 和 的调制。采用去混合主成分分析（dPCA）评估群体水平对力和抓握的调谐，并从神经活动中预测这些参数。这项工作有三个主要发现：（1）力信息在神经上得到表示，并可以在多个手抓握中进行解码（在一名参与者中，也可以在尝试伸展肘部时进行解码）；（2）抓握类型会影响多单位神经特征中的力表示以及离线力分类准确性；（3）抓握的分类准确性更高，并且比力具有更大的群体水平表示。这些发现表明，力和抓握在皮层内都具有独立和相互作用的表示，如果解码器还考虑到抓握类型，则可以在多个手抓握中实现力控制到实时 iBCI 系统。
