Institute of Neuroinformatics, University of Zürich and Eidgenössisch Technische Hochschule Zürich, CH-8057 Zürich, Switzerland.
J Neurosci. 2011 Oct 5;31(40):14386-98. doi: 10.1523/JNEUROSCI.2451-11.2011.
Despite recent advances in harnessing cortical motor-related activity to control computer cursors and robotic devices, the ability to decode and execute different grasping patterns remains a major obstacle. Here we demonstrate a simple Bayesian decoder for real-time classification of grip type and wrist orientation in macaque monkeys that uses higher-order planning signals from anterior intraparietal cortex (AIP) and ventral premotor cortex (area F5). Real-time decoding was based on multiunit signals, which had similar tuning properties to cells in previous single-unit recording studies. Maximum decoding accuracy for two grasp types (power and precision grip) and five wrist orientations was 63% (chance level, 10%). Analysis of decoder performance showed that grip type decoding was highly accurate (90.6%), with most errors occurring during orientation classification. In a subsequent off-line analysis, we found small but significant performance improvements (mean, 6.25 percentage points) when using an optimized spike-sorting method (superparamagnetic clustering). Furthermore, we observed significant differences in the contributions of F5 and AIP for grasp decoding, with F5 being better suited for classification of the grip type and AIP contributing more toward decoding of object orientation. However, optimum decoding performance was maximal when using neural activity simultaneously from both areas. Overall, these results highlight quantitative differences in the functional representation of grasp movements in AIP and F5 and represent a first step toward using these signals for developing functional neural interfaces for hand grasping.
尽管最近在利用皮质运动相关活动来控制计算机光标和机器人设备方面取得了进展，但解码和执行不同抓握模式的能力仍然是一个主要障碍。在这里，我们展示了一种简单的贝叶斯解码器，用于实时分类猕猴的握法类型和手腕方向，该解码器使用前顶内皮层（AIP）和腹侧运动前皮层（F5 区）的高级规划信号。实时解码基于多单位信号，这些信号与之前的单细胞记录研究中的细胞具有相似的调谐特性。两种抓握类型（力握和精确握）和五个手腕方向的最大解码精度为 63%（机会水平为 10%）。对解码器性能的分析表明，握法类型的解码非常准确（90.6%），大多数错误发生在方向分类过程中。在随后的离线分析中，我们发现使用优化的尖峰排序方法（超顺磁聚类）时，性能有较小但显著的提高（平均提高 6.25 个百分点）。此外，我们观察到 F5 和 AIP 在抓握解码方面的贡献存在显著差异，F5 更适合分类握法类型，而 AIP 对物体方向的解码贡献更大。然而，当同时使用两个区域的神经活动时，最佳解码性能达到最大值。总的来说，这些结果突出了 AIP 和 F5 中抓握运动功能表示的定量差异，代表了朝着使用这些信号开发用于手抓握的功能性神经接口迈出的第一步。
