A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis

Figure 1

Scheme of the spike sorting processing algorithms incorporated in this work. For each electrode the raw signal is preprocessed before the subsequent spike detection by a threshold-based algorithm (i.e., AdaBandFlt [24]). The feature extraction can be performed with four different methods (i.e., principal component analysis (PCA), Discrete Wavelet Transform (DWT), geometric features (GEO), and First and Second Derivative Extrema (FSDE)) followed by a dimensionality reduction step that retains the relevant features. Three clustering algorithms are implemented to automatically cluster spike features (i.e., -means, fuzzy-C-means (FCM), and density-based clustering (DBC)). As an alternative, a template matching algorithm (O-sort) groups the spikes as soon as they are detected.