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A linear-Discriminant-Analysis-Based Approach to Enhance the Performance of Fuzzy C-means Clustering in Spike Sorting With low-SNR Data
Chien-Wen Cho, Wen-Hung Chao, You-Yin Chen
Pages - 1 - 13     |    Revised - 15-06-2007     |    Published - 30-06-2007
Volume - 1   Issue - 1    |    Publication Date - June 2007  Table of Contents
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KEYWORDS
Spike sorting, spike classification, fuzzy c-means, principal-component analysis, linear discriminant analysis, low-SNR.
ABSTRACT
Spike sorting is of prime importance in neurophysiology and hence has received considerable attention. However, conventional methods suffer from the degradation of clustering results in the presence of high levels of noise contamination. This paper presents a scheme for taking advantage of automatic clustering and enhancing the feature extraction efficiency, especially for low-SNR spike data. The method employs linear discriminant analysis based on a fuzzy c-means (FCM) algorithm. Simulated spike data [1] were used as the test bed due to better a priori knowledge of the spike signals. Application to both high and low signal-to-noise ratio (SNR) data showed that the proposed method outperforms conventional principal-component analysis (PCA) and FCM algorithm. FCM failed to cluster spikes for low-SNR data. For two discriminative performance indices based on Fisher's discriminant criterion, the proposed approach was over 1.36 times the ratio of between- and within-class variation of PCA for spike data with SNR ranging from 1.5 to 4.5 dB. In conclusion, the proposed scheme is unsupervised and can enhance the performance of fuzzy c-means clustering in spike sorting with low-SNR data.
CITED BY (1)  
1 T. Rashid, “Classification of Churn and non-Churn Customers in Telecommunication Companies” International Journal of Biometrics and Bioinformatics (IJBB), 3(5), pp. 66-95, Nov. 2009.
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Mr. Chien-Wen Cho
- Taiwan
Mr. Wen-Hung Chao
- Taiwan
Mr. You-Yin Chen
- Taiwan
irradiance@so-net.net.tw


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