Spike sorting using Superlets: Evaluation of a novel feature space for the discrimination of neuronal spikes

Because of the intricacy of neural data, spike sorting is a challenging problem in neuroscience. Overlapping clusters are one of the hardest issues to solve, along with the similarity of spike shapes, excessive noise, and unbalanced clusters. Here, we focus on analyzing the efficacy and integrating the Superlets Transform as a feature extraction method. Our results indicate that Superlets achieve not only super-resolution in time and frequency, but also provide the conditions for smooth clustering performance. The Superlet Transform was analyzed in conjunction with Principal Component Analysis, Singular Value Decomposition, and Isomap Embedding as dimensionality reduction methods. The Superlet-extracted features were classified using Neural Networks in order to assess their relevance.