Classification of neuronal spikes over the reconstructed phase space.
Spike information is beneficial to correlate neuronal activity to various stimuli or determine target neural area for deep brain stimulation. Data clustering based on neuronal spike features provides a way to separate spikes generated from different neurons. Nevertheless, some spikes are aligned incorrectly due to spike deformation or noise interference, thereby reducing the accuracy of spike classification. In the present study, we proposed unsupervised spike classification over the reconstructed phase spaces of neuronal spikes in which the derived phase space portraits are less affected by alignment deviations. Principal component analysis was used to extract major principal components of the portrait features and k-means clustering was used to distribute neuronal spikes into various clusters. Finally, similar clusters were iteratively merged based upon inter-cluster portrait differences.
Chan
Hsiao-Lung
HL
Department of Electrical Engineering, Chang Gung University, Taoyuan, Taiwan. chanhl@mail.cgu.edu.tw <chanhl@mail.cgu.edu.tw>
Wu
Tony
T
Lee
Shih-Tseng
ST
Fang
Shih-Chin
SC
Chao
Pei-Kuang
PK
Lin
Ming-An
MA
D000200:Action Potentials / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000145:classification* / Q000502:physiology*; D010300:Parkinson Disease / Q000473:pathology*; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted; D013378:Substantia Nigra / Q000473:pathology; D020531:Subthalamic Nucleus / Q000473:pathology