A Template-Based Sequential Algorithm for Online Clustering of Spikes in Extracellular Recordings

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.