自适应SpikeDeep-Classifier：用于在线spike排序的自组织和自监督机器学习算法
Adaptive SpikeDeep-Classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting
AI总结：
提出自适应在线spike排序算法Ada-SpikeDeepClassifier，通过通道选择、背景活动拒绝和自适应分类处理密集微电极数据，在多个数据集上优于现有算法，适合神经形态芯片硬件实现。
摘要：
目的。脑机接口（BCI）的研究正朝着在现实世界中康复重度残疾患者的方向发展。成功解码用户意图的两个关键因素是植入式微电极阵列的规模和良好的在线spike排序算法。最近开发了一种具有3072通道的小型密集微电极阵列，用于解码用户意图。spike排序的过程从记录的神经数据中确定不同来源（神经元）的spike活动（SA）。不幸的是，当前的spike排序算法无法处理密集微电极阵列产生的大量数据，使得spike排序成为在线BCI解码框架中脆弱的组件。方法。我们提出了一种用于在线spike排序的自适应自组织算法，名为Adaptive SpikeDeep-Classifier（Ada-SpikeDeepClassifier），它使用SpikeDeeptometer进行通道选择，使用自适应背景活动拒绝器（Ada-BAR）丢弃背景事件，并使用自适应spike分类器（Ada-Spike classifier）对不同神经单元的SA进行分类。结果。在人类数据集和公开可用的模拟数据集上评估，我们的算法优于我们之前发布的SpikeDeep-Classifier和其他八种spike排序算法。意义。所提出的算法是第一个自动学习噪声和SA分布突变的spike排序算法。它是一种基于人工神经网络的算法，非常适合在神经形态芯片上进行硬件实现，可用于可穿戴侵入式BCI。
英文摘要：
Objective. Research on brain-computer interfaces (BCIs) is advancing towards rehabilitating severely disabled patients in the real world. Two key factors for successful decoding of user intentions are the size of implanted microelectrode arrays and a good online spike sorting algorithm. A small but dense microelectrode array with 3072 channels was recently developed for decoding user intentions. The process of spike sorting determines the spike activity (SA) of different sources (neurons) from recorded neural data. Unfortunately, current spike sorting algorithms are unable to handle the massively increasing amount of data from dense microelectrode arrays, making spike sorting a fragile component of the online BCI decoding framework. Approach. We proposed an adaptive and self-organized algorithm for online spike sorting, named Adaptive SpikeDeep-Classifier (Ada-SpikeDeepClassifier), which uses SpikeDeeptector for channel selection, an adaptive background activity rejector (Ada-BAR) for discarding background events, and an adaptive spike classifier (Ada-Spike classifier) for classifying the SA of different neural units. Results. Our algorithm outperformed our previously published SpikeDeep-Classifier and eight other spike sorting algorithms, as evaluated on a human dataset and a publicly available simulated dataset. Significance. The proposed algorithm is the first spike sorting algorithm that automatically learns the abrupt changes in the distribution of noise and SA. It is an artificial neural network-based algorithm that is well-suited for hardware implementation on neuromorphic chips that can be used for wearable invasive BCIs.
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