Department of Neurology, Northwestern University Chicago, IL 60611, USA.
J Neural Eng. 2011 Jun;8(3):036013. doi: 10.1088/1741-2560/8/3/036013. Epub 2011 Apr 21.
Brain-machine interfaces (BMIs) use signals from the brain to control a device such as a computer cursor. Various types of signals have been used as BMI inputs, from single-unit action potentials to scalp potentials. Recently, intermediate-level signals such as subdural field potentials have also shown promise. These different signal types are likely to provide different amounts of information, but we do not yet know what signal types are necessary to enable a particular BMI function, such as identification of reach target location, control of a two-dimensional cursor or the dynamics of limb movement. Here we evaluated the performance of field potentials, measured either intracortically (local field potentials, LFPs) or epidurally (epidural field potential, EFPs), in terms of the ability to decode reach direction. We trained rats to move a joystick with their forepaw to control the motion of a sipper tube to one of the four targets in two dimensions. We decoded the forelimb reach direction from the field potentials using linear discriminant analysis. We achieved a mean accuracy of 69 ± 3% with EFPs and 57 ± 2% with LFPs, both much better than chance. Signal quality remained good up to 13 months after implantation. This suggests that using epidural signals could provide BMI inputs of high quality with less risk to the patient than using intracortical recordings.
脑机接口 (BMI) 使用来自大脑的信号来控制设备，例如计算机光标。已经使用了各种类型的信号作为 BMI 输入，从单个单元动作电位到头皮电位。最近，亚表面场电位等中间水平信号也显示出了前景。这些不同的信号类型可能提供不同数量的信息，但我们还不知道哪些信号类型是实现特定 BMI 功能所必需的，例如识别目标位置、控制二维光标或肢体运动的动态。在这里，我们评估了场电位的性能，这些场电位是通过皮层内（局部场电位，LFPs）或皮层下（硬膜外场电位，EFPs）测量的，其能力是解码伸手方向。我们训练大鼠用前爪移动操纵杆，以控制吸吮管在二维空间中的四个目标之一的运动。我们使用线性判别分析从场电位中解码前肢伸手方向。我们使用 EFPs 实现了 69 ± 3%的平均准确率，使用 LFPs 实现了 57 ± 2%的平均准确率，均远高于随机水平。信号质量在植入后 13 个月内仍然良好。这表明，使用硬膜外信号可以提供高质量的 BMI 输入，对患者的风险低于使用皮层内记录。
