Department of Neuroscience, Northwestern University, Chicago, IL, United States of America.
Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America.
J Neural Eng. 2023 Nov 1;20(5):056040. doi: 10.1088/1741-2552/ad038e.
. Intracortical brain-computer interfaces (iBCIs) aim to enable individuals with paralysis to control the movement of virtual limbs and robotic arms. Because patients' paralysis prevents training a direct neural activity to limb movement decoder, most iBCIs rely on 'observation-based' decoding in which the patient watches a moving cursor while mentally envisioning making the movement. However, this reliance on observed target motion for decoder development precludes its application to the prediction of unobservable motor output like muscle activity. Here, we ask whether recordings of muscle activity from a surrogate individual performing the same movement as the iBCI patient can be used as target for an iBCI decoder.. We test two possible approaches, each using data from a human iBCI user and a monkey, both performing similar motor actions. In one approach, we trained a decoder to predict the electromyographic (EMG) activity of a monkey from neural signals recorded from a human. We then contrast this to a second approach, based on the hypothesis that the low-dimensional 'latent' neural representations of motor behavior, known to be preserved across time for a given behavior, might also be preserved across individuals. We 'transferred' an EMG decoder trained solely on monkey data to the human iBCI user after using Canonical Correlation Analysis to align the human latent signals to those of the monkey.. We found that both direct and transfer decoding approaches allowed accurate EMG predictions between two monkeys and from a monkey to a human.. Our findings suggest that these latent representations of behavior are consistent across animals and even primate species. These methods are an important initial step in the development of iBCI decoders that generate EMG predictions that could serve as signals for a biomimetic decoder controlling motion and impedance of a prosthetic arm, or even muscle force directly through functional electrical stimulation.
. 皮层内脑机接口（iBCI）旨在使瘫痪患者能够控制虚拟肢体和机械臂的运动。由于患者的瘫痪使得无法直接对神经活动进行训练来解码肢体运动，因此大多数 iBCI 依赖于“基于观察的解码”，患者在进行想象运动时观看移动的光标。然而，这种对观察到的目标运动的依赖限制了其在预测不可观察的运动输出（如肌肉活动）方面的应用。在这里，我们想知道是否可以使用来自替代个体的肌肉活动记录来作为 iBCI 解码器的目标，该个体执行与 iBCI 患者相同的运动。. 我们测试了两种可能的方法，每种方法都使用来自人类 iBCI 用户和猴子的数据，这两个个体都执行类似的运动动作。在一种方法中，我们训练一个解码器，以便从记录自人类的神经信号中预测猴子的肌电图（EMG）活动。然后，我们将其与第二种方法进行对比，该方法基于这样的假设，即运动行为的低维“潜在”神经表示在给定行为中会随时间保持不变，并且可能在个体之间也保持不变。我们使用典型相关分析将人类的潜在信号与猴子的信号对齐后，将仅在猴子数据上训练的 EMG 解码器“转移”到人类 iBCI 用户。. 我们发现，直接和转移解码方法都可以在两只猴子之间以及从一只猴子到一个人类之间实现准确的 EMG 预测。. 我们的发现表明，这些行为的潜在表示在动物之间甚至灵长类动物之间是一致的。这些方法是开发 iBCI 解码器的重要初始步骤，这些解码器可以生成 EMG 预测，这些预测可以作为信号用于仿生解码器来控制假肢手臂的运动和阻抗，甚至可以直接通过功能性电刺激来控制肌肉力量。
