Real time controller for brain machine interface experiment

我们要求一种能够确保脑机接口的闭合环路到达实验的确定性行为的系统。我们的脑机接口方法是独一无二的，因为我们的目标是教大脑使用我们的算法，而不是编写解释神经代码的算法。我们正在进行的实验需要实时连续的反馈来诱导顶叶和运动前皮质的可塑性。这种范式对指令和反馈之间的延迟非常敏感。我们已经训练了一只非人类灵长类动物(NHP)来快速和实时地改变神经元的调谐。在每一次试验中，NHP都必须考虑以这样一种方式到达，以便屏幕上的目标被放大。目标的大小代表了我们破译这一特定审判的能力。一旦NHP了解了这项任务，在面对猴子的屏幕上解码多达12个位置的过程中，有了迅速而显著的改进。然而，我们受到反馈信号延迟的阻碍。如果延迟变得太大，条件反射就会中断。我们已经看到，在反馈延迟时间达到峰值后，性能有所下降。这个问题阻碍和阻碍了我们的进步。我们请求的NetLinx系统通过消除路径中的所有第二和第三计算机来消除反馈中的延迟。该系统将所有统计和信号处理算法直接传输到运行LabVIEW Real Time的行为控制器上。这一系统将被大量使用，因为它是我们实验继续成功不可或缺的一部分。

We are requesting a system that is capable of ensuring deterministic behaviour of closed loop reach experiments for brain machine interfaces. Our approach to brain machine interfaces is unique as our goal is to teach the brain to use our algorithms rather than write algorithms that interpret the neural code. The experiments we are conducting require real time continuous feedback to induce plasticity in the parietal and premotor cortices. This paradigm is very sensitive to delays between the instruction and the feedback. We have trained a non human primate (NHP) to change the tuning of neurons rapidly and in real time. Each trial, the NHP has to think about reaching in such a way so that a target on the screen is inflated. The size of the target is a proxy for our ability to decode that particular trial. Once the NHP understood the task, there was rapid and considerable improvement in decoding up to 12 positions on a screen that faces the monkey. However, we are hindered by delays in the feedback signal. Conditioning is disrupted if delays become too big. We have seen a decrease in performance after a spike in the feedback delay time. This problem has frustrated and hindered our progress. The NetLinx system we are requesting eliminates the delay in the feedback by eliminating all secondary and tertiary computers in the pathway. This system transfers all the statistical and signal processing algorithms directly onto our behavioural controller which runs Labview Real Time. This system will be heavily utilized as it is integral for the continued success of our experiments.