A Grid-based Valley Seeking Method for Spike Sorting
A new density- and grid-based clustering algorithm is proposed to identifying free shape clusters. The proposed algorithm is a non-parametric method, which does not require user specifying parameters for clustering. The algorithm divides each dimension of the data space into certain intervals to form a grid structure. The valley seeking procedure is employed to find the cluster centers where the data density is higher than neighbor grids and to initialize clusters. Then, the discrimination between any two clusters is evaluated by Fishers linear discriminant, and cluster pairs which dont have a density valley between them are merged. Compared with many conventional algorithms, this algorithm is computational efficient because it clusters data by grids rather than by points. The accuracy and efficient of the proposed algorithm was verified on extracellular recorded neural spikes.
Spike Sorting grid clustering valley seeking linear discriminant
Xiao-qin Liu Xing Wu Chang Liu
Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming, China
国际会议
上海
英文
591-594
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）