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

大脑皮质内脑机接口(IBCI)被用来直接记录来自人的 大脑，根据这些信号预测他们的意图，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。这项技术严重地使 瘫痪的人们与世界互动。然而，设计健壮的算法来提取意图 单个神经元的记录是极具挑战性的，很大程度上是因为获得 人类，甚至猴子，这些侵入性的录音都可以从他们身上制作出来。 在这个项目中，我们将开发一个模型IBCI系统，它通过以下方式生成实时仿生神经数据 捕捉健全人体受试者的高自由度手指动作。要做到这一点， 我们将构造一个模块化递归神经网络(RNN)。RNN将被训练来预测马达 猴子的大脑皮层活动来自猴子自己的手指运动学。RNN的小模块将是 根据特定的动物或记录会话互换以模拟会话间的高变异性 出现在运动皮质。一旦模块RNN被训练，它的权重将被固定，并且人的手指 运动学将被用作RNN输入，它将产生受试者控制的仿真神经活动。这个 模拟的神经活动可以传递给IBCI解码算法，该算法控制计算机游标或其他 物理设备，允许人类受试者与解码器实时、闭环直接交互 条件。我们称这个模型系统为JABCI。JABCI是低成本和非侵入性的，使其有可能 使用严格的样本大小快速测试和设计新型IBCI解码器。 该项目将与皮质内微电极阵列数据专家Lee博士密切合作执行 米勒在西北大学工作。米勒博士的实验室，在我们顾问马西斯博士的帮助下，将获得 猴子受试者同时的手指运动学和神经活动，将作为 IBCI模型的RNN组件。 我们将通过多种措施验证由jaBCI生成的模拟神经数据，以确保模型 捕捉尽可能多的皮质内数据特征。这包括比较模型和实际情况 IBCI在受试者成绩、学习率、控制策略、神经日间变异性、神经放电率 分布和低维神经动力学。利用经过验证的模型，我们将进行一项研究，以 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 提供最大性能收益的解码器的特性，克服了目前不可能 在控制良好的研究中，使用两到三个天真的人类受试者来比较IBCI解码器。我们会 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.