Deep Metric Learning With Locality Sensitive Mining for Self-Correcting Source Separation of Neural Spiking Signals
Deep Metric Learning With Locality Sensitive Mining for Self-Correcting Source Separation of Neural Spiking Signals
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DOI：
10.1109/tcyb.2023.3290825
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发表时间：
2023-07
影响因子：
11.8
通讯作者：
A. Clarke;D. Farina
中科院分区：
文献类型：
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作者：
A. Clarke;D. Farina
自动源分离算法已成为神经工程和神经科学的核心工具，用于将神经生理信号分解为其组成的尖峰信号源。然而，在有噪声或高度多变量的记录中，这些分解技术往往会产生大量的错误。这种错误降低了在线人机界面方法，并需要在离线设置中进行昂贵的事后手动清理。在本文中，我们提出了一种使用深度度量学习(DML)框架的自动纠错方法，生成嵌入空间，在其中可以识别尖峰事件并将其分配给各自的源。此外，我们调查了不同的DML技术在保留神经生理时间序列中识别错误类别标签所需的类内语义结构方面的相对能力。在这一分析的基础上，我们提出了位置敏感挖掘，这是一种易于实现的基于抽样的典型DML损失的扩充，大大改善了嵌入空间的局部语义结构。我们展示了这种方法的有效性，可以生成嵌入空间，用于高精度地自动识别错误标记的尖峰事件。
Automated source separation algorithms have become a central tool in neuroengineering and neuroscience, where they are used to decompose neurophysiological signal into its constituent spiking sources. However, in noisy or highly multivariate recordings these decomposition techniques often make a large number of errors. Such mistakes degrade online human-machine interfacing methods and require costly post-hoc manual cleaning in the offline setting. In this article we propose an automated error correction methodology using a deep metric learning (DML) framework, generating embedding spaces in which spiking events can be both identified and assigned to their respective sources. Furthermore, we investigate the relative ability of different DML techniques to preserve the intraclass semantic structure needed to identify incorrect class labels in neurophysiological time series. Motivated by this analysis, we propose locality sensitive mining, an easily implemented sampling-based augmentation to typical DML losses which substantially improves the local semantic structure of the embedding space. We demonstrate the utility of this method to generate embedding spaces which can be used to automatically identify incorrectly labeled spiking events with high accuracy.