Christie Breanne P, Tat Derek M, Irwin Zachary T, Gilja Vikash, Nuyujukian Paul, Foster Justin D, Ryu Stephen I, Shenoy Krishna V, Thompson David E, Chestek Cynthia A
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
J Neural Eng. 2015 Feb;12(1):016009. doi: 10.1088/1741-2560/12/1/016009. Epub 2014 Dec 11.
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.
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.
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.
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.
对于皮层内脑机接口(BMI),动作电位电压波形通常需要进行分类以分离出单个神经元。如果这些神经元包含独立的调谐信息,那么这个过程可能会提高BMI的性能。然而,动作电位(“尖峰”)的分类需要高采样率,并且计算成本很高。为了明确界定尖峰分类与其他方法之间的差异,我们在使用过阈值事件与分类动作电位时,对BMI解码器的性能进行了量化。
我们使用了来自两只植入犹他阵列的恒河猴的58个实验会话的数据集。在动物执行具有七个不同角度的中心向外伸展任务时记录数据。对于尖峰分类,通过使用高斯混合对波形的前四个主成分进行聚类,将神经信号分类为单个单元。对于过阈值事件,仅保留简单越过设定阈值的尖峰。我们使用朴素贝叶斯分类器对到达方向进行离线数据解码,并使用线性回归来评估手部位置。
我们发现在将阈值设置在-3至-4.5×Vrms之间时,过阈值方法的性能最高。对于一只动物,分类后的尖峰数据表现优于过阈值数据,但对另一只动物则不然。分类数据的朴素贝叶斯平均分类准确率为88.5%,当过阈值处理时平均变化5%。分类数据关联系数的平均值为0.92,当过阈值处理时平均变化0.015。
对于假肢应用,这些结果表明,当使用过阈值处理而非尖峰分类时,可能只会损失少量性能。利用过阈值事件可能会显著延长设备的使用寿命,因为一旦单个神经元不再分离,这些事件通常仍可检测到。