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.

翻译：目标。脑机接口（BCI）研究正朝着在现实世界中帮助重度残疾患者康复的方向发展。解码用户意图的两个关键因素是植入式微电极阵列的尺寸以及高效的在线脉冲分类算法。最近开发了一种包含3072通道的小型高密度微电极阵列用于解码用户意图。脉冲分类过程旨在从记录的神经数据中确定不同来源（神经元）的脉冲活动（SA）。然而，现有脉冲分类算法无法处理高密度微电极阵列产生的海量数据增长，这使得脉冲分类成为在线BCI解码框架中的薄弱环节。方法。我们提出了一种自适应自组织的在线脉冲分类算法，命名为自适应脉冲深度分类器（Ada-SpikeDeepClassifier）。该算法采用SpikeDeeptector进行通道选择，通过自适应背景活动抑制器（Ada-BAR）剔除背景事件，并利用自适应脉冲分类器（Ada-Spike分类器）对不同神经单元SA进行分类。结果。在人类数据集和公开模拟数据集上的评估表明，我们的算法优于此前发表的SpikeDeep-Classifier及其他八种脉冲分类算法。意义。所提算法是首个能自动学习噪声与SA分布突变特征的脉冲分类算法。该算法基于人工神经网络，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脑机接口。