Direct feature extraction from multi-electrode recordings for spike sorting

Information from extracellular action potentials (EAPs) of individual neurons is of particular interest in experimental neuroscience. It advances the understanding of brain functions and is essential in the emerging field of brain-machine interfaces. As EAPs from distinct neurons are generally not recorded individually, a process to separate them from the multi-unit recordings, referred to as spike sorting. is required. For spike sorting, the feature extraction step is crucial. Starting from acquired data, the task of feature extraction is to find a set of derived values or "features" that are informative and non redundant to facilitate efficient and accurate sorting, compared with using the raw data directly. It not only reduces the dimensionality of the data but also the impact of noise. In this paper, two novel feature extraction algorithms for sorting multi-electrode EAPs are proposed. These algorithms can be seen as generalizations of principal component analysis and linear discriminant analysis, but the features that match the dominant subspaces observed in the multi-electrode data are obtained without the need for vectorizing a multi-electrode EAP or breaking it into separate EAP channels. These algorithms require no construction of EAP templates and are applicable to multi-electrode recordings regardless of the number of electrodes. Clustering using both simulated data and real EAP recordings taken from area CM of the dorsal hippocampus of rats demonstrates that the proposed approaches yield features that are discriminatory and lead to promising results. (C) 2018 Elsevier Inc. All rights reserved.