  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.
