  Spikes in the membrane electrical potentials of neurons play a major role in
the functioning of nervous systems of animals. Obtaining the spikes from
different neurons has been a challenging problem for decades. Several schemes
have been proposed for spike sorting to isolate the spikes of individual
neurons from electrical recordings in extracellular media. However, there is
much scope for improvement in the accuracies obtained using the prevailing
methods of spike sorting. To determine more effective spike sorting strategies
using well known methods, we compared different types of signal features and
techniques for dimensionality reduction in feature space. We tried to determine
an optimum or near optimum feature extraction and dimensionality reduction
methods and an optimum or near optimum number of features for spike sorting. We
assessed relative performance of well known methods on simulated recordings
specially designed for development and benchmarking of spike sorting schemes,
with varying number of spike classes and the well established method of
$k$-means clustering of selected features. We found that almost all well known
methods performed quite well. Nevertheless, from spike waveforms of 64 samples,
sampled at 24 kHz, using principal component analysis (PCA) to select around 46
to 55 features led to the better spike sorting performance than most other
methods (Wilcoxon signed rank sum test, $p < 0.001$).
