The spike signal is extracted by weighted principal component analysis to extract features and to reduce the dimension. The features are clustered by Gaussian mixture model clustering algorithm. Spike sorting is achieved. Open simulation data and open measured data are used to analyze and verify the classification precision and feasibility of the algorithm and compared with the two methods of the Gaussion mixture model（ GMM）clustering of the principal component analysis（ PCA） and the K-means clustering of weighted PCA. In simulation data experiment

when the noise levels are 0. 05

0. 10

0. 15

and 0. 20

the misclassification rates are 1. 26 %

1. 43 %

2. 32 %

and 3. 37 %

respectively

whic

h are lower than the other two methods. In the measured data experiment

the average J

3

criterion value of the Rhesus monkey data is 14. 12. Compared with the other two methods

the average J

3

criterion value is larger.