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:摘要:BCI领域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大小和良好的在线SPIKE分类算法。最近为解码用户意图而开发了一个拥有3072个通道的小而密集的微电极阵列。SPIKE分类的过程是确定不同源(神经元)的SPIKE活动(SA)并从记录的神经数据中提取这些信息。不幸的是,当前的SPIKE分类算法无法处理来自密集微电极阵列的大量数据,使SPIKE分类成为在线BCI解码框架中不可或缺的组成部分。我们提出了一种自适应和自组织的在线SPIKE分类算法,名为Adaptive SpikeDeep-Classifier(Ada-SpikeDeepClassifier),其使用SpikeDeeptector进行通道选择,自适应背景活动拒绝者(Ada-BAR)进行丢弃背景事件,并使用自适应SPIKE分类器(Ada-Spike分类器)对不同神经元的SA进行分类。我们的算法在人类数据集和公开可用的模拟数据集上表现优于我们之前的发布的SpikeDeep-Classifier和其他八种SPIKE分类算法。所提出的算法是第一个能够自动学习噪声和SA分布的突发变化的SPIKE分类算法。这是一种基于人工神经网络的算法,非常适合硬件实现在可穿戴式植入式BCI的神经形态芯片上。