Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance

被引:65
作者
Christie, Breanne P. [1 ]
Tat, Derek M. [1 ]
Irwin, Zachary T. [1 ]
Gilja, Vikash [2 ,3 ,4 ]
Nuyujukian, Paul [5 ,6 ]
Foster, Justin D. [7 ]
Ryu, Stephen I. [7 ,8 ]
Shenoy, Krishna V. [5 ,7 ,9 ,10 ]
Thompson, David E. [1 ,11 ]
Chestek, Cynthia A. [1 ,12 ,13 ]
机构
[1] Univ Michigan, Dept Biomed Engn, Ann Arbor, MI 48109 USA
[2] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA
[3] Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA
[4] Univ Calif San Diego, Neurosci Program, La Jolla, CA 92093 USA
[5] Stanford Univ, Dept Bioengn, Stanford, CA 94305 USA
[6] Stanford Univ, Sch Med, Stanford, CA 94305 USA
[7] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA
[8] Palo Alto Med Fdn, Dept Neurosurg, Palo Alto, CA 94301 USA
[9] Stanford Univ, Neurosci Program, Stanford, CA 94305 USA
[10] Stanford Univ, Dept Neurobiol, Stanford, CA 94305 USA
[11] Kansas State Univ, Dept Elect & Comp Engn, Manhattan, KS 66506 USA
[12] Univ Michigan, Neurosci Program, Ann Arbor, MI 48109 USA
[13] Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA
基金
美国国家科学基金会;
关键词
spike sorting; threshold; brain-machine interface; LOCAL-FIELD POTENTIALS; CORTICAL CONTROL; CONTROL SIGNALS; MOVEMENT; MOTOR; COMPUTER; GRASP; REACH; ARM;
D O I
10.1088/1741-2560/12/1/016009
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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 Naive 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 x V-rms. Spike sorted data outperformed thresholded data for one animal but not the other. The mean Naive 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.
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页数:10
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