title: A novel framework for feature extraction in multi-sensor action potential sorting.
abstract: BACKGROUND
  Extracellular recordings of multi-unit neural activity have become indispensable in neuroscience research. The analysis of the recordings begins with the detection of the action potentials (APs), followed by a classification step where each AP is associated with a given neural source. A feature extraction step is required prior to classification in order to reduce the dimensionality of the data and the impact of noise, allowing source clustering algorithms to work more efficiently.
  
  
  METHODS
  In this paper, we propose a novel framework for multi-sensor AP feature extraction based on the so-called Matched Subspace Detector (MSD), which is shown to be a natural generalization of standard single-sensor algorithms.
  
  
  RESULTS
  Clustering using both simulated data and real AP recordings taken in the locust antennal lobe demonstrates that the proposed approach yields features that are discriminatory and lead to promising results.
  
  
  CONCLUSIONS
  Unlike existing methods, the proposed algorithm finds joint spatio-temporal feature vectors that match the dominant subspace observed in the two-dimensional data without needs for a forward propagation model and AP templates.
  
  
  CONCLUSIONS
  The proposed MSD approach provides more discriminatory features for unsupervised AP sorting applications.
authors.affiliation: Department of Engineering and System Science, National Tsing Hua University, Hsinchu 30013, Taiwan, ROC. Electronic address: shunchi.wu@mx.nthu.edu.tw.
authors.forename: Shun-Chi
authors.initials: SC
authors.lastname: Wu
authors.affiliation: Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA.
authors.forename: A Lee
authors.initials: AL
authors.lastname: Swindlehurst
authors.affiliation: Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA.
authors.forename: Zoran
authors.initials: Z
authors.lastname: Nenadic
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D003198:Computer Simulation; D004566:Electrodes; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D058067:Wavelet Analysis
keywords: Dimensionality reduction; Feature extraction; Spike detection; Spike sorting