Graph-spectrum-based neural spike features for stereotrodes and tetrodes

Extracellular recording of neural signals records the action potentials (known as spikes) of neurons adjacent to the electrode as well as the noise generated by the overall neural activity around the electrode. Analysis of these spikes is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper introduces a new feature extraction algorithm for neural spike sorting to isolate single neuronal units out of multi-unit activity when multiple closely-spaced electrodes (two for a stereotrode, four for a tetrode) are used. The proposed algorithm, which is inspired by spectral graph theory, simultaneously minimizes the graph-Laplacian and maximizes the variance. Real test signals from stereotrode and tetrode recordings show that the proposed approach outperforms the most commonly-used feature extraction methods, including Principal Components Analysis (PCA) and ratios of peak spike amplitudes between different electrodes of a stereotrode or tetrode.