Unsupervised feature selection by kernel density estimation in wavelet-based spike sorting

摘要：

　　Wavelet transform has been widely applied in extracting characteristic information in spike sorting.As the wavelet coefficients used to distinguish various spike shapes are often disorganized, there still lacks in effective unsupervised methods to select the most discriminative features.In this paper, we propose an unsupervised feature selection method, employing kernel density estimation to select those wavelet coefficients with bimodal or multimodal distributions.This method is tested on a simulated spike data set, and the average misclassification rate after fuzzy C-means clustering has been greatly reduced, which proves this kernel density estimation based feature selection approach is effective in selecting the most discriminative features.

关键词： spike sorting wavelet transform unsupervised feature selection kernel density estimation

作者: Xinling Geng Guangshu Hu

作者单位: School of Biomedical Engineering, Capital Medical University, 100069, Beijing, China Department of Biomedical Engineering, Tsinghua University, 100084, Beijing, China

会议类型: 国内会议

会议名称: 首都医科大学生物医学工程学科学术年会

会议地点: 北京

会议语种:英文

页码: 62-74

在线出版日期: 2012-12-01（万方平台首次上网日期，不代表论文的发表时间）