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. APPROACH: 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. MAIN 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. SIGNIFICANCE: 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.
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. APPROACH: 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. MAIN 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. SIGNIFICANCE: 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: John P Cunningham; Paul Nuyujukian; Vikash Gilja; Cindy A Chestek; Stephen I Ryu; Krishna V Shenoy Journal: J Neurophysiol Date: 2010-10-13 Impact factor: 2.714
Authors: Cynthia A Chestek; Vikash Gilja; Paul Nuyujukian; Ryan J Kier; Florian Solzbacher; Stephen I Ryu; Reid R Harrison; Krishna V Shenoy Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2009-06-02 Impact factor: 3.802
Authors: Sagi Perel; Patrick T Sadtler; Emily R Oby; Stephen I Ryu; Elizabeth C Tyler-Kabara; Aaron P Batista; Steven M Chase Journal: J Neurophysiol Date: 2015-07-01 Impact factor: 2.714
Authors: Zhonghua Ouyang; Zachariah J Sperry; Nikolas D Barrera; Tim M Bruns Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2019-04-22 Impact factor: 3.802
Authors: Beata Jarosiewicz; Anish A Sarma; Jad Saab; Brian Franco; Sydney S Cash; Emad N Eskandar; Leigh R Hochberg Journal: J Physiol Paris Date: 2017-03-08
Authors: Eric M Trautmann; Sergey D Stavisky; Subhaneil Lahiri; Katherine C Ames; Matthew T Kaufman; Daniel J O'Shea; Saurabh Vyas; Xulu Sun; Stephen I Ryu; Surya Ganguli; Krishna V Shenoy Journal: Neuron Date: 2019-06-03 Impact factor: 17.173
Authors: Nir Even-Chen; Dante G Muratore; Sergey D Stavisky; Leigh R Hochberg; Jaimie M Henderson; Boris Murmann; Krishna V Shenoy Journal: Nat Biomed Eng Date: 2020-08-03 Impact factor: 25.671
Authors: Emily R Oby; Sagi Perel; Patrick T Sadtler; Douglas A Ruff; Jessica L Mischel; David F Montez; Marlene R Cohen; Aaron P Batista; Steven M Chase Journal: J Neural Eng Date: 2016-04-21 Impact factor: 5.379
Authors: Vikash Gilja; Chethan Pandarinath; Christine H Blabe; Paul Nuyujukian; John D Simeral; Anish A Sarma; Brittany L Sorice; János A Perge; Beata Jarosiewicz; Leigh R Hochberg; Krishna V Shenoy; Jaimie M Henderson Journal: Nat Med Date: 2015-09-28 Impact factor: 53.440
Authors: Beata Jarosiewicz; Anish A Sarma; Daniel Bacher; Nicolas Y Masse; John D Simeral; Brittany Sorice; Erin M Oakley; Christine Blabe; Chethan Pandarinath; Vikash Gilja; Sydney S Cash; Emad N Eskandar; Gerhard Friehs; Jaimie M Henderson; Krishna V Shenoy; John P Donoghue; Leigh R Hochberg Journal: Sci Transl Med Date: 2015-11-11 Impact factor: 17.956