Modern brain-machine interfaces can return function to people with paralysis, but current upper extremity brain-machine interfaces are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a non-prehensile task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined. During online brain control, the ReFIT Kalman filter could predict individuated finger group movements with high performance. Next, training ridge regression decoders with individual movements was sufficient to predict untrained combined movements and vice versa. Finally, we compared the postural and movement tuning of finger-related cortical activity to find that individual cortical units simultaneously encode multiple behavioral dimensions. Our results suggest that linear decoders may be sufficient for brain-machine interfaces to execute high-dimensional tasks with the performance levels required for naturalistic neural prostheses.

现代脑机接口可以让瘫痪的人恢复功能，但目前的上肢脑机接口无法重现对个体手指运动的控制。在这里，我们首次在非人类灵长类动物中展示了一种实时、高速、线性的脑机接口，该接口利用皮层内的神经信号来弥合这一差距。我们创建了一个非抓握任务，系统地将两个手指组分开，食指和中指和小指相结合。在在线大脑控制期间，ReFIT Kalman 滤波器可以高性能地预测单个手指组的运动。接下来，使用单个动作训练岭回归解码器足以预测未经训练的组合动作，反之亦然。最后，我们比较了手指相关皮层活动的姿势和运动调整，发现单个皮层单位同时编码多个行为维度。我们的结果表明，线性解码器可能足以让脑机接口执行具有自然神经假体所需性能水平的高维任务。