Real time controller for brain machine interface experiment

脑机接口实验实时控制器

• 批准号: 406373-2011
• 负责人: Musallam, Wissam
• 金额: $ 7.07万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments - Category 1 (<$150,000)
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=472453
• 关键词: Real; time; controller; brain; machine

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.

我们要求一个系统，能够确保闭环达到脑机接口实验的确定性行为。我们的脑机接口方法是独一无二的，因为我们的目标是教会大脑使用我们的算法，而不是编写算法来解释神经代码。我们正在进行的实验需要实时的连续反馈来诱导顶叶和运动前皮层的可塑性。这种模式对指令和反馈之间的延迟非常敏感。我们已经训练了一种非人类灵长类动物（NHP）来快速实时地改变神经元的调谐。每次试验，NHP都必须考虑以这样一种方式到达，这样屏幕上的目标就会膨胀。目标的大小代表了我们解码特定试验的能力。一旦NHP理解了这个任务，在解码面向猴子的屏幕上多达12个位置的能力上就有了迅速而可观的进步。然而，我们受到反馈信号延迟的阻碍。如果延迟太大，条件反射就会中断。我们看到在反馈延迟时间达到峰值后，性能会下降。这个问题阻碍了我们的进步。我们要求的NetLinx系统通过消除路径中的所有二级和三级计算机来消除反馈中的延迟。该系统将所有的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。这个系统将被大量利用，因为它是我们实验持续成功的组成部分。