Wavelet footprints for detection and sorting of extracellular neural action potentials

Spike detection and sorting is a fundamental step in the analysis of extracellular neural recording. Here, we propose a combined spike detection-feature extraction algorithm that relies on a sparse representation space of the spike waveforms. The proposed method captures the wavelet footprint of the waveform, by calculating the power of the scale space vectors and finding an optimal detection threshold using histogram equalization techniques. Under the proposed scheme, a compact feature set is obtained simultaneously during detection, which eliminates the need for a separate feature extraction step for spike sorting. Our results demonstrate that this method yields improved performance, particularly in low SNRs, while preserving the separability between neuronal clusters in the feature space.