Neurophysiological time series, such as electromyographic signal and intracortical recordings, are typically composed of many individual spiking sources, the recovery of which can give fundamental insights into the biological system of interest or provide neural information for man-machine interfaces. For this reason, source separation algorithms have become an increasingly important tool in neuroscience and neuroengineering. However, in noisy or highly multivariate recordings these decomposition techniques often make a large number of errors, which degrades human-machine interfacing applications and often requires costly post-hoc manual cleaning of the output label set of spike timestamps. To address both the need for automated post-hoc cleaning and robust separation filters we propose a methodology based on deep metric learning, using a novel loss function which maintains intra-class variance, creating a rich embedding space suitable for both label cleaning and the discovery of new activations. We then validate this method with an artificially corrupted label set based on source-separated high-density surface electromyography recordings, recovering the original timestamps even in extreme degrees of feature and class-dependent label noise. This approach enables a neural network to learn to accurately decode neurophysiological time series using any imperfect method of labelling the signal.

翻译：神经生理时间序列,例如电子感应信号和园内记录,通常由许多个人弹射源组成,其恢复可以从根本上洞察生物系统,或为人机界面提供神经信息。为此原因,源分离算法已成为神经科学和神经工程中日益重要的工具。然而,在噪音或高度多变的录音中,这些分解技术往往造成大量错误,使人体机器互毁应用发生退化,并常常需要花费昂贵的热后人工清理钉钉钉钉时标集。为了解决自动热后清洁和稳健分离过滤的需要,我们建议了一种基于深层计量学习的方法,使用一种新的损失函数,维持阶级内部差异,创造出一个丰富的嵌入空间,既适合清理标签,又适合发现新的激活。我们随后用一种人为腐败的标签来验证这种方法,该标签基于源分离的高密度表面电感学记录,恢复原始时间标记,甚至以极端程度的特征和等级不完善的神经物理标签序列。这样可以使神经物理标签网络能够准确地学习任何神经元的升级方法。