An Intracortical Brain-Computer Interface Model for High Efficiency Development of Closed-Loop Neural Decoding Algorithms

皮质内脑机接口（iBCI）用于记录直接来自人的大脑皮层的电信号。 大脑，从这些信号中预测他们的意图，然后控制辅助设备（例如，计算机光标， 假肢，或电动轮椅）根据这些意图。这项技术使严重 瘫痪的人与世界互动。然而，设计鲁棒的算法来从 单个神经元的记录是非常具有挑战性的，在很大程度上是因为非常有限的访问， 人类，甚至猴子，这些侵入性的记录可以从他们身上获得。 在这个项目中，我们将开发一个模型iBCI系统，通过以下方式生成实时仿生神经数据： 捕捉身体健全的人类受试者的高自由度手指运动。为了实现这一点， 我们将构造一个模块化递归神经网络（RNN）。RNN将被训练来预测电机 从猴子自己手指的运动学来观察猴子大脑皮层的活动。RNN的小模块将被 根据特定动物或记录会话互换，以模拟会话间的高变异性 存在于运动皮层。一旦模块化RNN被训练好，它的权重将被固定， 运动学将被用作RNN输入，这将生成受试者控制的仿真神经活动。的 仿真的神经活动可以被传递到控制计算机光标或其他设备的iBCI解码算法。 物理设备，允许人类主体在真实的时间内直接与解码器交互，闭环 条件我们称这个模型系统为jaBCI。jaBCI成本低且无创，使得有可能 使用统计上严格的样本量快速测试和设计新颖的iBCI解码器。 该项目将与皮质内微电极阵列数据专家李博士密切合作执行 西北大学的米勒。米勒博士的实验室在我们的顾问马西斯博士的帮助下 同时手指运动学和神经活动的猴子科目，将作为训练数据， iBCI模型的RNN组件。 我们将在许多措施中验证jaBCI生成的仿真神经数据，以确保模型 捕获尽可能多的皮质内数据特征。其中包括比较模型和实际 iBCI在受试者表现，学习率，控制策略，神经变化，神经放电率 分布和低维神经动力学。我们会根据经验证的模式进行研究， 严格评估最高性能、当前最先进的iBCI解码器。这将产生有用的见解 解码器的功能，产生最大的性能增益，克服目前的不可能， 在使用两个或三个以上的幼稚人类受试者的良好对照研究中比较iBCI解码器。我们将 我还使用iBCI模型来评估新的解码器设计，并确定神经动力学的特征 这些功能在常见的iBCI任务中是一致的，以帮助将解码器开发集中在这些功能上。

An intracortical brain-computer interface (iBCI) is used to record electrical signals directly from a person's brain, predict their intention from those signals, then control an assistive device (e.g., a computer cursor, prosthetic limb, or powered wheelchair) according to those intentions. This technology enables severely paralyzed people to interact with the world. However, designing robust algorithms to extract intent from recordings of single neurons is extremely challenging, in large part because of the very limited access to humans, or even monkeys, from whom these invasive recordings can be made. In this project, we will develop a model iBCI system that generates real-time biomimetic neural data by capturing the high-degree-of-freedom finger movements of able-bodied human subjects. To accomplish this, we will construct a modular recurrent neural network (RNN). The RNN will be trained to predict the motor cortex activity of a monkey from the monkey's own finger kinematics. Small modules of the RNN will be interchanged according the particular animal or recording session to model the high inter-session variability present in motor cortex. Once the modular RNN is trained, its weights will be fixed and human finger kinematics will be used as the RNN inputs, which will generate subject-controlled emulated neural activity. The emulated neural activity can be passed to iBCI decoding algorithms that control computer cursors or other physical devices, allowing human subjects to interact directly with decoders in real time, closed-loop conditions. We call this model system the jaBCI. The jaBCI is low cost and noninvasive, making it possible to rapidly test and design novel iBCI decoders using statistically rigorous sample sizes. The project will be executed in close collaboration with intracortical microelectrode array data expert Dr. Lee Miller at Northwestern University. Dr. Miller's lab, with the help of our consultant Dr. Mathis, will obtain simultaneous finger kinematics and neural activity of monkey subjects that will serve as the training data for the RNN component of the iBCI model. We will validate the emulated neural data generated by the jaBCI across many measures to ensure the model captures as many features of intracortical data as possible. These include comparing the model and actual iBCI in subject performance, learning rates, control strategies, neural variation across days, neural firing rate distributions, and low-dimensional neural dynamics. With the validated model, we will undertake a study to rigorously evaluate the highest performing, current state-of-the-art iBCI decoders. This will yield useful insight into the features of decoders that yield the greatest performance gains, overcoming the current impossibility to compare iBCI decoders in well-controlled studies using more than two or three naïve human subjects. We will also use the iBCI model to evaluate novel decoder designs, and to determine the features of neural dynamics that are consistent across common iBCI tasks to help focus decoder development on those features.