Journal Article


R B Reilly
J J H Miller
A Della-Chiesa
V Hok
Shane Michael O'Mara
E Chah



action potentials algorithms feature extraction k means algorithm physiology clustering algorithm cluster analysis databases factual medical physics computational physics models neurological biological physics k means clustering principal component analysis neurons

Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering (2011)

Abstract This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximization) were incorporated within the spike sorting algorithms, in order to find a suitable classifier for the feature sets. Simulated data sets and in-vivo tetrode multichannel recordings were employed to assess the performance of the spike sorting algorithms. The results show that the proposed algorithm yields significantly improved performance with mean sorting accuracy of 73% and sorting error of 10% compared to PCA which combined with k-means had a sorting accuracy of 58% and sorting error of 10%.
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Full list of authors on original publication

R B Reilly, J J H Miller, A Della-Chiesa, V Hok, Shane Michael O'Mara, E Chah

Experts in our system

Richard Reilly
Trinity College Dublin
Total Publications: 185
Shane Michael O'Mara
Trinity College Dublin
Total Publications: 63