title: Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance.
abstract: OBJECTIVE
  For intracortical brain-machine interfaces (BMIs), action potential voltage waveforms are often sorted to separate out individual neurons. If these neurons contain independent tuning information, this process could increase BMI performance. However, the sorting of action potentials ('spikes') requires high sampling rates and is computationally expensive. To explicitly define the difference between spike sorting and alternative methods, we quantified BMI decoder performance when using threshold-crossing events versus sorted action potentials.
  
  
  METHODS
  We used data sets from 58 experimental sessions from two rhesus macaques implanted with Utah arrays. Data were recorded while the animals performed a center-out reaching task with seven different angles. For spike sorting, neural signals were sorted into individual units by using a mixture of Gaussians to cluster the first four principal components of the waveforms. For thresholding events, spikes that simply crossed a set threshold were retained. We decoded the data offline using both a Naïve Bayes classifier for reaching direction and a linear regression to evaluate hand position.
  
  
  RESULTS
  We found the highest performance for thresholding when placing a threshold between -3 and -4.5 × Vrms. Spike sorted data outperformed thresholded data for one animal but not the other. The mean Naïve Bayes classification accuracy for sorted data was 88.5% and changed by 5% on average when data were thresholded. The mean correlation coefficient for sorted data was 0.92, and changed by 0.015 on average when thresholded.
  
  
  CONCLUSIONS
  For prosthetics applications, these results imply that when thresholding is used instead of spike sorting, only a small amount of performance may be lost. The utilization of threshold-crossing events may significantly extend the lifetime of a device because these events are often still detectable once single neurons are no longer isolated.
authors.affiliation: Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Breanne P
authors.initials: BP
authors.lastname: Christie
authors.forename: Derek M
authors.initials: DM
authors.lastname: Tat
authors.forename: Zachary T
authors.initials: ZT
authors.lastname: Irwin
authors.forename: Vikash
authors.initials: V
authors.lastname: Gilja
authors.forename: Paul
authors.initials: P
authors.lastname: Nuyujukian
authors.forename: Justin D
authors.initials: JD
authors.lastname: Foster
authors.forename: Stephen I
authors.initials: SI
authors.lastname: Ryu
authors.forename: Krishna V
authors.initials: KV
authors.lastname: Shenoy
authors.forename: David E
authors.initials: DE
authors.lastname: Thompson
authors.forename: Cynthia A
authors.initials: CA
authors.lastname: Chestek
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001931:Brain Mapping / Q000379:methods*; D062207:Brain-Computer Interfaces; D003627:Data Interpretation, Statistical; D008253:Macaca mulatta; D009044:Motor Cortex / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity