Brain-computer interfaces (BCIs) are an emerging strategy for spinal cord injury (SCI) intervention that may be used to reanimate paralyzed limbs. This approach requires decoding movement intention from the brain to control movement-evoking stimulation. Common decoding methods use spike-sorting and require frequent calibration and high computational complexity. Furthermore, most applications of closed-loop stimulation act on peripheral nerves or muscles, resulting in rapid muscle fatigue. Here we show that a local field potential-based BCI can control spinal stimulation and improve forelimb function in rats with cervical SCI. We decoded forelimb movement via multi-channel local field potentials in the sensorimotor cortex using a canonical correlation analysis algorithm. We then used this decoded signal to trigger epidural spinal stimulation and restore forelimb movement. Finally, we implemented this closed-loop algorithm in a miniaturized onboard computing platform. This Brain-Computer-Spinal Interface (BCSI) utilized recording and stimulation approaches already used in separate human applications. Our goal was to demonstrate a potential neuroprosthetic intervention to improve function after upper extremity paralysis.

中文翻译：

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脑-机-脊髓接口可恢复脊髓损伤后的上肢功能


脑机接口（BCIs）是一种新兴的脊髓损伤（SCI）干预策略，可用于复活瘫痪的肢体。这种方法需要解码大脑的运动意图来控制运动诱发的刺激。常见的解码方法使用尖峰排序，需要频繁的校准和高计算复杂度。此外，闭环刺激的大多数应用作用于周围神经或肌肉，导致肌肉快速疲劳。在这里，我们证明基于局部场电位的 BCI 可以控制脊髓刺激并改善颈椎 SCI 大鼠的前肢功能。我们使用典型相关分析算法通过感觉运动皮层中的多通道局部场电位解码前肢运动。然后，我们使用该解码信号来触发硬膜外脊髓刺激并恢复前肢运动。最后，我们在小型化的机载计算平台上实现了这种闭环算法。这种脑-计算机-脊髓接口（BCSI）利用了已经在单独的人类应用中使用的记录和刺激方法。我们的目标是证明一种潜在的神经假体干预措施可以改善上肢瘫痪后的功能。