Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option to restore voluntary movements after paralysis. These devices are based on the ability to extract information about movement intent from neural signals recorded using multi-electrode arrays chronically implanted in the motor cortices of the brain. However, the inherent loss and turnover of recorded neurons requires repeated recalibrations of the interface, which can potentially alter the day-to-day user experience. The resulting need for continued user adaptation interferes with the natural, subconscious use of the BMI. Here, we introduce a new computational approach that decodes movement intent from a low-dimensional latent representation of the neural data. We implement various domain adaptation methods to stabilize the interface over significantly long times. This includes Canonical Correlation Analysis used to align the latent variables across days; this method requires prior point-to-point correspondence of the time series across domains. Alternatively, we match the empirical probability distributions of the latent variables across days through the minimization of their Kullback-Leibler divergence. These two methods provide a significant and comparable improvement in the performance of the interface. However, implementation of an Adversarial Domain Adaptation Network trained to match the empirical probability distribution of the residuals of the reconstructed neural signals outperforms the two methods based on latent variables, while requiring remarkably few data points to solve the domain adaptation problem.
翻译:脑- 脑- 脑- 脑界面( BMIs) 最近在临床上成为恢复瘫痪后自愿运动的一种可行的临床选择。 这些装置基于能够从神经信号中提取关于运动意图的信息。 这些神经信号是用大脑运动皮层中长期植入的多电子电极阵列记录下来的。 但是, 记录下来的神经元的固有损失和循环需要反复校正界面, 这可能改变用户的日常经验。 因此, 用户的持续适应需要干扰BMI的自然、 潜意识使用。 这里, 我们引入了一种新的计算方法, 从神经数据的低维潜代表中解码运动意图。 我们采用了各种域调适方法, 以便长期稳定该界面的界面。 这包括用于连续调整潜在变量的Canonical Coollation 分析; 这种方法需要时间序列的点对点对点对点对点对应。 或者, 我们通过最大限度地减少其 Kull- Lever 差异来匹配潜在变量的经验概率分布。 这两种方法提供了显著和可比的移动移动意图, 并且提供了在经过培训的网络的轨道上进行稳定性调整的概率分配的方法 。 Do 。