title: Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering.
abstract: BACKGROUND
  Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice.
  
  
  METHODS
  An automatic unsupervised spike sorting method is proposed in this paper. The method uses features extracted by the locality preserving projection (LPP) algorithm. These features afterwards serve as inputs for the landmark-based spectral clustering (LSC) method. Gap statistics (GS) is employed to evaluate the number of clusters before the LSC can be performed.
  
  
  RESULTS
  The proposed LPP-LSC is highly accurate and computationally inexpensive spike sorting approach. LPP spike features are very discriminative; thereby boost the performance of clustering methods. Furthermore, the LSC method exhibits its efficiency when integrated with the cluster evaluator GS.
  
  
  METHODS
  The proposed method's accuracy is approximately 13% superior to that of the benchmark combination between wavelet transformation and superparamagnetic clustering (WT-SPC). Additionally, LPP-LSC computing time is six times less than that of the WT-SPC.
  
  
  CONCLUSIONS
  LPP-LSC obviously demonstrates a win-win spike sorting solution meeting both accuracy and computational cost criteria. LPP and LSC are linear algorithms that help reduce computational burden and thus their combination can be applied into real-time spike analysis.
authors.affiliation: Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds Campus, Victoria 3216, Australia. Electronic address: thanh.nguyen@deakin.edu.au.
authors.forename: Thanh
authors.initials: T
authors.lastname: Nguyen
authors.affiliation: Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds Campus, Victoria 3216, Australia.
authors.forename: Abbas
authors.initials: A
authors.lastname: Khosravi
authors.affiliation: Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds Campus, Victoria 3216, Australia.
authors.forename: Douglas
authors.initials: D
authors.lastname: Creighton
authors.affiliation: Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds Campus, Victoria 3216, Australia.
authors.forename: Saeid
authors.initials: S
authors.lastname: Nahavandi
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D016000:Cluster Analysis; D009474:Neurons / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D058067:Wavelet Analysis
keywords: Feature extraction; Gap statistics; Landmark-based spectral clustering; Locality preserving projection; Spike sorting; Superparamagnetic clustering; Wavelet transformation