Latent variable models for neural data analysis
Latent variable models for neural data analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
A spike detection method in EEG based on improved morphological filter
Computers in Biology and Medicine
IWINAC'05 Proceedings of the First international conference on Mechanisms, Symbols, and Models Underlying Cognition: interplay between natural and artificial computation - Volume Part I
Design of prefilters for discrete multiwavelet transforms
IEEE Transactions on Signal Processing
The application of multiwavelet filterbanks to image processing
IEEE Transactions on Image Processing
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Neural spike sorting is an indispensable first step for the analysis of multiple spike train data. As it is very common that noises degrade the performance of most of the available spike sorting method, in this paper we have proposed a novel spike sorting algorithm framework which seems less susceptible to heavy noises. At first, mathematical morphology operation is used to facilitate the spike event detection process, especially in strong noisy situations. Then, multiwavelets transform is performed to the detected spike waveforms to extract discriminative features. Finally, hierarchical clustering with an outlier removal process proceeds to separate the first 10 distinguishable multiwavelets coefficients. The results show that our spike sorting method performs quite well even for the heavy noisy simulated spike data.