Abstract:
: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.
journal_name
J Neural Engjournal_title
Journal of neural engineeringauthors
Lai HY,Chen YY,Lin SH,Lo YC,Tsang S,Chen SY,Zhao WT,Chao WH,Chang YC,Wu R,Shih YY,Tsai ST,Jaw FSdoi
10.1088/1741-2560/8/3/036003subject
Has Abstractpub_date
2011-06-01 00:00:00pages
036003issue
3eissn
1741-2560issn
1741-2552pii
S1741-2560(11)73366-8journal_volume
8pub_type
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