Image Processing - Principles and Applications
Image Processing - Principles and Applications
Robustness of neural spike sorting to sampling rate and quantization bit depth
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Stability of neural firing in the trigeminal nuclei under mechanical whisker stimulation
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Disentanglement of local field potential sources by independent component analysis
Journal of Computational Neuroscience
A system for behavior prediction based on neural signals
Neurocomputing
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Sorting of the extracellularly recorded spikes is a basic prerequisite for analysis of the cooperative neural behavior and neural code. Fundamentally the sorting performance is defined by the quality of discriminative features extracted from spike waveforms. Here we discuss two features extraction approaches: principal component analysis (PCA), and wavelet transform (WT). We show that only when properly tuned to the data, the WT technique may outperform PCA. We present a novel method for extraction of spike features based on a combination of PCA and continuous WT. The method automatically tunes its WT part to the data structure making use of knowledge obtained by PCA. We demonstrate the method on simulated and experimental data sets.