title: To sort or not to sort: the impact of spike-sorting on neural decoding performance.
abstract: OBJECTIVE
  Brain-computer interfaces (BCIs) are a promising technology for restoring motor ability to paralyzed patients. Spiking-based BCIs have successfully been used in clinical trials to control multi-degree-of-freedom robotic devices. Current implementations of these devices require a lengthy spike-sorting step, which is an obstacle to moving this technology from the lab to the clinic. A viable alternative is to avoid spike-sorting, treating all threshold crossings of the voltage waveform on an electrode as coming from one putative neuron. It is not known, however, how much decoding information might be lost by ignoring spike identity.
  
  
  METHODS
  We present a full analysis of the effects of spike-sorting schemes on decoding performance. Specifically, we compare how well two common decoders, the optimal linear estimator and the Kalman filter, reconstruct the arm movements of non-human primates performing reaching tasks, when receiving input from various sorting schemes. The schemes we tested included: using threshold crossings without spike-sorting; expert-sorting discarding the noise; expert-sorting, including the noise as if it were another neuron; and automatic spike-sorting using waveform features. We also decoded from a joint statistical model for the waveforms and tuning curves, which does not involve an explicit spike-sorting step.
  
  
  RESULTS
  Discarding the threshold crossings that cannot be assigned to neurons degrades decoding: no spikes should be discarded. Decoding based on spike-sorted units outperforms decoding based on electrodes voltage crossings: spike-sorting is useful. The four waveform based spike-sorting methods tested here yield similar decoding efficiencies: a fast and simple method is competitive. Decoding using the joint waveform and tuning model shows promise but is not consistently superior.
  
  
  CONCLUSIONS
  Our results indicate that simple automated spike-sorting performs as well as the more computationally or manually intensive methods used here. Even basic spike-sorting adds value to the low-threshold waveform-crossing methods often employed in BCI decoding.
authors.affiliation: Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
authors.forename: Sonia
authors.initials: S
authors.lastname: Todorova
authors.forename: Patrick
authors.initials: P
authors.lastname: Sadtler
authors.forename: Aaron
authors.initials: A
authors.lastname: Batista
authors.forename: Steven
authors.initials: S
authors.lastname: Chase
authors.forename: Valérie
authors.initials: V
authors.lastname: Ventura
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004562:Electrocardiography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D008253:Macaca mulatta; D008959:Models, Neurological; D015233:Models, Statistical; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
references.citation: Neural Netw. 2009 Nov;22(9):1203-13
references.pmid: 19502004
references.citation: J Neurosci Methods. 2006 Jun 30;154(1-2):204-24
references.pmid: 16488479
references.citation: IEEE Trans Neural Syst Rehabil Eng. 2012 Jul;20(4):468-77
references.pmid: 22772374
references.citation: Nature. 2006 Jul 13;442(7099):164-71
references.pmid: 16838014
references.citation: Neural Comput. 2009 Sep;21(9):2466-501
references.pmid: 19548802
references.citation: PLoS Biol. 2003 Nov;1(2):E42
references.pmid: 14624244
references.citation: Nat Neurosci. 2012 Dec;15(12):1752-7
references.pmid: 23160043
references.citation: J Physiol. 2007 Mar 15;579(Pt 3):581-601
references.pmid: 17255162
references.citation: Nature. 2008 Jun 19;453(7198):1098-101
references.pmid: 18509337
references.citation: Neural Comput. 2006 Jan;18(1):80-118
references.pmid: 16354382
references.citation: Neural Comput. 2008 Apr;20(4):923-63
references.pmid: 18085990
references.citation: Nature. 2012 May 16;485(7398):372-5
references.pmid: 22596161
references.citation: J Neurosci. 2007 Aug 1;27(31):8387-94
references.pmid: 17670985
references.citation: Network. 1998 Nov;9(4):R53-78
references.pmid: 10221571
references.citation: Neural Comput. 2011 Dec;23(12):3162-204
references.pmid: 21919788
references.citation: IEEE Trans Biomed Eng. 2014 Jan;61(1):41-54
references.pmid: 23912463
references.citation: Neural Comput. 2004 Aug;16(8):1661-87
references.pmid: 15228749
references.citation: J Neural Eng. 2011 Aug;8(4):045005
references.pmid: 21775782
references.citation: J Neurophysiol. 2000 Jul;84(1):401-14
references.pmid: 10899214
references.citation: Neural Comput. 2004 Feb;16(2):277-307
references.pmid: 15006097
references.citation: J Neurophysiol. 2014 Jan;111(1):217-27
references.pmid: 24089403
references.citation: J Neural Eng. 2009 Oct;6(5):055004
references.pmid: 19721186
references.citation: Lancet. 2013 Feb 16;381(9866):557-64
references.pmid: 23253623
references.citation: J Neurosci. 2010 Dec 15;30(50):16777-87
references.pmid: 21159949
references.citation: Proc Natl Acad Sci U S A. 2009 Apr 28;106(17):6921-6
references.pmid: 19372379
references.citation: J Am Stat Assoc. 2010 Mar;105(489):170-180
references.pmid: 21753862
references.citation: J Neurophysiol. 2004 Apr;91(4):1899-907
references.pmid: 15010499
references.citation: J Neurophysiol. 2012 Apr;107(7):1970-8
references.pmid: 22190623
references.citation: Nature. 2002 Mar 14;416(6877):141-2
references.pmid: 11894084
references.citation: J Neurosci. 2008 Nov 26;28(48):12913-26
references.pmid: 19036985
references.citation: J Neurophysiol. 2005 Jul;94(1):8-25
references.pmid: 15985692
references.citation: J Neurosci Methods. 2002 May 30;117(1):1-12
references.pmid: 12084559
references.citation: IEEE Trans Biomed Eng. 2007 Jan;54(1):82-93
references.pmid: 17260859