A Novel Feature Extractor Based on Wavelet and Kernel PCA for Spike Sorting Neural Signals
Spike sorting is often required for analyzing neural recordings to isolate the activity of single neurons. In this paper, a new feature extractor based on Wavelet and kernel PCA for spike sorting was proposed. Electrophysiology recordings were made in Sprague-Dawley (SD) rats to provide neural signals. Here, an adaptive threshold based on the duty-cycle keeping method was used to detect spike and a new spike alignment technique was used to decrease sampling skew error. After spikes were detected and alimented, to extract spike features, their wavelet transform was calculated, the first 10 coefficients with the largest deviation from normality provided a compressed representation of the spike features that serves as the input to KPCA algorithm. Once the features have been extracted, k-means clustering was utilised to separate the features and differentiate the spikes. Test results with simulated data files and data obtained from SD rats in vivo showed an excellent classification result, indicating the good performance of the described algorithm approach.
KeywordsSpike sorting kernel PCA Wavelet k-means clustering
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