Classification of neuronal spikes over the reconstructed phase space

Hsiao Lung Chan*, Tony Wu, Shih Tseng Lee, Shih Chin Fang, Pei Kuang Chao, Ming An Lin

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

17 Scopus citations


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.

Original languageEnglish
Pages (from-to)203-211
Number of pages9
JournalJournal of Neuroscience Methods
Issue number1
StatePublished - 15 02 2008


  • Cluster mergence
  • Phase space reconstruction
  • Principal component analysis
  • Spike alignment
  • Spike classification
  • k-Means clustering


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