In order to discriminate different spikes in an extracellular recording, a multitude of successful spike sorting algorithms has been proposed up to now. However, new implantable neuroprosthetics containing a spike sorting block necessitate the use of a real-time and a preferably unsupervised method. The aim of this article is to propose a new unsupervised spike sorting algorithm which could work in real-time. As opposed to most traditional frameworks that consist of separate noise cancelation and feature extraction steps, here a sequential algorithm is proposed which makes use of noise statistics and uses data samples as features. For each detected spike, the difference between the detected spike and all the previously detected spike templates are calculated. If the output is a signal similar to noise, this indicates that the new spike is fired from a previously observed neuron. Two varieties of the general method are illustrated and a set of clustering indices which determine an optimal clustering is used to set the parameters. Clustering indices surpassed 0.90 (out of 1) for synthetic data with modest noise level. Experiments with our recorded signals showed satisfactory results in clustering and template identification. Spike sorting is an active field. A deficiency in conventional spike sorting algorithms is that most of them are either supervised or offline. Here, we present an online unsupervised algorithm which could be developed as a solution for current neuroprosthetics. Since the present method clustered real spikes data appropriately without a need for training data, the methodology could be adapted to be used in implantable devices.

中文翻译：

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基于模板的细胞外记录穗的在线聚类顺序算法

为了区分细胞外记录中的不同尖峰，迄今为止已经提出了许多成功的尖峰分类算法。然而，包含尖峰分选块的新的可植入神经假体需要使用实时且优选无监督的方法。本文的目的是提出一种可以实时工作的新型无监督尖峰排序算法。与由分离的噪声消除和特征提取步骤组成的大多数传统框架相反，此处提出一种顺序算法，该算法利用噪声统计量并将数据样本用作特征。对于每个检测到的尖峰，计算检测到的尖峰与所有先前检测到的尖峰模板之间的差。如果输出是类似于噪声的信号，这表明从先前观察到的神经元发射了新的尖峰。说明了两种通用方法，并使用一组确定最佳聚类的聚类索引来设置参数。对于噪声水平适中的合成数据，聚类指数超过0.90（满分为1）。使用我们记录的信号进行的实验在聚类和模板识别方面显示出令人满意的结果。穗排序是一个活跃的领域。传统的尖峰排序算法的不足之处在于，大多数算法都是有监督的或离线的。在这里，我们提出了一种在线无监督算法，可以将其开发为当前神经假体的解决方案。由于本方法不需要训练数据就可以适当地对实际峰值数据进行聚类，因此该方法可以适用于可植入设备。