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. 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. Results We found the highest performance for thresholding when placing a threshold between −3 to −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 was 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.

Sums of Spike Waveform Features for Motor Decoding

“…We confirm previous findings that the hash should not be discarded, as it substantially increases decoding accuracy when included along with sorted spike counts (Todorova et al, 2014; Christie et al, 2015; Oby et al, 2016). Since sorted spike counts traditionally do not include hash, while threshold crossing counts do include hash, a comparison between these two methods is actually comparing two factors: presence of hash and use of spike sorting.…”

Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters

“…This is not surprising given that in offline analyses multiunit activity and threshold crossings have yielded decoding performance and encoding fidelity that is comparable to or better than sorted spikes or local field potentials (Stark and Abeles 2007, Ventura 2008, Chestek et al 2011, Kloosterman et al 2014, Malik et al 2014, Todorova et al 2014, Christie et al 2015, Perel et al 2015). Recently, we and others have begun to recognize the need to investigate threshold setting in a principled way.…”

“…Recently, we and others have begun to recognize the need to investigate threshold setting in a principled way. Christie and colleagues (Christie et al 2015) found optimal thresholds for decoding performance to be between 3–4.5 times the rms voltage ( V rms ). Importantly, they only considered threshold settings from 3–18 × V rms ; they did not consider threshold crossings at lower threshold settings.…”

Clinical translation of a high-performance neural prosthesis

“…In contrast, the current study (T6 and T7) used simpler threshold crossing counts as neural features. These features demonstrated nearly equivalent decoding performance and the potential for greater stability in NHP studies 21,22 and were used in a previous clinical study 1 .…”