Combination of PCA and undecimated wavelet transform for neural data processing

Nervous system conveys information by electrical signals called `spikes´, therefore, spikes detection and sorting are challenging topics in the neural data processing. The principal component analysis (PCA) is a convenient tool for clustering spikes; however it has some disadvantages for closely shaped and overlapped spikes. For such the cases, an algorithm based on the combination of the principal component analysis and undecimated wavelet transform, is proposed to enhance the cluster formation from the spikes mapping. These results indicate that the principal component analysis used in combination with the undecimated wavelet has a better performance in the spike sorting. This can lead to more compact and separate clusters in comparison with the PCA clustering and more efficient spike sorting.