標題: Automatic spike sorting for extracellular electrophysiological recording using unsupervised single linkage clustering based on grey relational analysis
作者: Lai, Hsin-Yi
Chen, You-Yin
Lin, Sheng-Huang
Lo, Yu-Chun
Tsang, Siny
Chen, Shin-Yuan
Zhao, Wan-Ting
Chao, Wen-Hung
Chang, Yao-Chuan
Wu, Robby
Shih, Yen-Yu I.
Tsai, Sheng-Tsung
Jaw, Fu-Shan
電機工程學系
Department of Electrical and Computer Engineering
公開日期: 1-六月-2011
摘要: Automatic spike sorting is a prerequisite for neuroscience research on multichannel extracellular recordings of neuronal activity. A novel spike sorting framework, combining efficient feature extraction and an unsupervised clustering method, is described here. Wavelet transform (WT) is adopted to extract features from each detected spike, and the Kolmogorov-Smirnov test (KS test) is utilized to select discriminative wavelet coefficients from the extracted features. Next, an unsupervised single linkage clustering method based on grey relational analysis (GSLC) is applied for spike clustering. The GSLC uses the grey relational grade as the similarity measure, instead of the Euclidean distance for distance calculation; the number of clusters is automatically determined by the elbow criterion in the threshold-cumulative distribution. Four simulated data sets with four noise levels and electrophysiological data recorded from the subthalamic nucleus of eight patients with Parkinson's disease during deep brain stimulation surgery are used to evaluate the performance of GSLC. Feature extraction results from the use of WT with the KS test indicate a reduced number of feature coefficients, as well as good noise rejection, despite similar spike waveforms. Accordingly, the use of GSLC for spike sorting achieves high classification accuracy in all simulated data sets. Moreover, J-measure results in the electrophysiological data indicating that the quality of spike sorting is adequate with the use of GSLC.
URI: http://dx.doi.org/10.1088/1741-2560/8/3/036003
http://hdl.handle.net/11536/23278
ISSN: 1741-2560
DOI: 10.1088/1741-2560/8/3/036003
期刊: JOURNAL OF NEURAL ENGINEERING
Volume: 8
Issue: 3
結束頁: 
顯示於類別:期刊論文


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