Universal Spike Classifier

Abstract

In electrophysiology, microelectrodes are the primary source for recording neural data of single neurons (single unit activity). These microelectrodes can be implanted individually, or in the form of microelectrodes arrays, consisting of hundreds of electrodes. During recordings, some channels capture the activity of neurons, which is usually contaminated with external artifacts and noise. Another considerable fraction of channels does not record any neural data, but external artifacts and noise. Therefore, an automatic identification and tracking of channels containing neural data is of great significance and can accelerate the process of analysis, e.g. automatic selection of meaningful channels during offline and online spike sorting. Another important aspect is the selection of meaningful channels during online decoding in brain-computer interface applications, where threshold crossing events are usually for feature extraction, even though they do not necessarily correspond to neural events. Here, we propose a novel algorithm based on the newly introduced way of feature vector extraction and a supervised deep learning method: a universal spike classifier (USC). The USC enables us to address both above-raised issues. The USC uses the standard architecture of convolutional neural networks (Conv net). It takes the batch of the waveforms, instead of a single waveform as an input, propagates it through the multilayered structure, and finally classifies it as a channel containing neural spike data or artifacts. We have trained the model of USC on data recorded from single tetraplegic patient with Utah arrays implanted in different brain areas. This trained model was then evaluated without retraining on the data collected from six epileptic patients implanted with depth electrodes and two tetraplegic patients implanted with two Utah arrays, individually.