Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance
Open accessAbstract
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.
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Threshold crossing rates and spike band power are estimates of local spiking activity and have been shown to be comparable to sorted single unit activity in terms of decoding performance and neural population structure.
“Threshold crossing rates and spike band power are estimates of local spiking activity and have been shown to be comparable to sorted single unit activity in terms of decoding performance and neural population structure79–81.”
From Inner speech in motor cortex and implications for speech neuroprostheses (2025) · cites this paper as [81]
The Discussion (page not provided) says thresholding and spike sorting can be used essentially interchangeably without a significant decrease in decoder performance, supporting the threshold-crossing comparison. The paper does not establish comparable decoding performance for spike-band power or compare neural population structure, so those parts of the claim are unsupported by this source. (LLM, conf=0.88)
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- Authors
- Christie, B. P., Tat, D. M., Irwin, Z. T., Gilja, V., Nuyujukian, P., Foster, J. D., Ryu, S. I., Shenoy, K. V., Thompson, D. E., & Chestek, C. A.
- Affiliations
- University of Michigan (US)
- University of California San Diego (US)
- Stanford Medicine (US)
- Stanford University (US)
- Palo Alto Medical Foundation (US)
- Kansas State University (US)
- ORCID
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2014-12-11
- Volume
- 12
- Issue
- 1
- Pages
- 016009
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- PMC
- PMC4332592
- OpenAlex
- W2005617065
- Cited by
- 103
- References
- 45
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 13 · 2023: 12 · 2022: 4 · 2021: 15 · 2020: 10 · 2019: 14 · 2018: 3 · 2017: 7 · 2016: 5 · 2015: 4
- FWCI
- 2.44
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- McKnight Endowment Fund for Neuroscience
- DARPA REPAIR contract (N66001-10-C-2010)
- DARPA Revolutionizing Prosthetics program contract (N66001-06-C-8005)
- National Science Foundation
- NIH Director’s Pioneer Award (DP1HD075623)
- Texas Instruments Inc.
- National Institutes of Health (CRCNS R01-NS054283)
- Burroughs Wellcome Fund
- Christopher Reeve Paralysis Foundation
- Stanford University
- American Society for Engineering Education
- Alfred P. Sloan Foundation
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- spike sorting, threshold crossing events, Utah array, rhesus monkeys, center-out reaching task, Naive Bayes classification, prosthetics, device lifetime
- MeSH terms
- Action Potentials, Algorithms, Animals, Brain Mapping, Data Interpretation, Statistical, Macaca mulatta, Motor Cortex, Nerve Net, Neurons, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Brain-Computer Interfaces
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