Longevity and reliability of chronic unit recordings using the Utah, intracortical multi-electrode arrays
Open accessAbstract
Objective. Microelectrode arrays are standard tools for conducting chronic electrophysiological experiments, allowing researchers to simultaneously record from large numbers of neurons. Specifically, Utah electrode arrays (UEAs) have been utilized by scientists in many species, including rodents, rhesus macaques, marmosets, and human participants. The field of clinical human brain-computer interfaces currently relies on the UEA as a number of research groups have clearance from the United States Federal Drug Administration (FDA) for this device through the investigational device exemption pathway. Despite its widespread usage in systems neuroscience, few studies have comprehensively evaluated the reliability and signal quality of the Utah array over long periods of time in a large dataset. Approach. We collected and analyzed over 6000 recorded datasets from various cortical areas spanning almost nine years of experiments, totaling 17 rhesus macaques (Macaca mulatta) and 2 human subjects, and 55 separate microelectrode Utah arrays. The scale of this dataset allowed us to evaluate the average life of these arrays, based primarily on the signal-to-noise ratio of each electrode over time. Main results. Using implants in primary motor, premotor, prefrontal, and somatosensory cortices, we found that the average lifespan of available recordings from UEAs was 622 days, although we provide several examples of these UEAs lasting over 1000 days and one up to 9 years; human implants were also shown to last longer than non-human primate implants. We also found that electrode length did not affect longevity and quality, but iridium oxide metallization on the electrode tip exhibited superior yield as compared to platinum metallization. Significance. Understanding longevity and reliability of microelectrode array recordings allows researchers to set expectations and plan experiments accordingly and maximize the amount of high-quality data gathered. Our results suggest that one can expect chronic unit recordings to last at least two years, with the possibility for arrays to last the better part of a decade.
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The Utah Microelectrode Array uses an external power mechanism and offers longevity with some lasting over 2.7 years and one case up to 9 years.
“Similarly, the Utah Microelectrode Array (UEA) uses an external power mechanism, offering longevity with some lasting over 2.7 years, and even one case up to 9 years (Sponheim et al., 2021).”
From Comparative analysis of energy transfer mechanisms for neural implants (2024) · cites this paper as Sponheim et al., 2021
The Abstract’s Main Results report several UEA recordings lasting over 1,000 days (more than 2.7 years) and one lasting up to nine years. The Methods describe cable-connected data acquisition, but do not establish the claim that the UEA uses an external power mechanism. (LLM, conf=0.86)
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Chen, Q., Huang, N., Tong, C., Liu, P., Que, S., Xue, H., Xiao, X., & Song, E. (2026) · npj Biosensing · doi:10.1038/s44328-026-00109-7 · Cited by 2 · see citing works
Motor Cortex Coverage Predicts Signal Strength of a Stentrode Endovascular Brain-Computer InterfacePreprint
Schone, H. R., Yoo, P. E., Fry, A., Chetty, N., Sawyer, A., Herbers, C., Liu, F., Moon, C., Hill, K. J., Majidi, S., et al. (2025) · medRxiv · doi:10.1101/2025.09.19.25335875 · Cited by 1 · see citing works
Self-citationLetner, J. G., Patel, P. R., Hsieh, J., Flores, I. M. S., della Valle, E., Walker, L. A., Weiland, J. D., Chestek, C. A., & Cai, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.07.511373
Which Ethical Issues Need to Be Considered Related to Microwires or Utah Arrays?
Young, M. J. (2023) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-031-20910-9_7
Development of quality criteria for long-term testing of intracortical electrodes
Sielaff, C., Urban, U., & Froriep, U. P. (2023) · Current Directions in Biomedical Engineering · doi:10.1515/cdbme-2023-1105
Long-Term Quality of Neural Signals: Criteria for Intracortical Implants
Charlotte, S., & Froriep, U. P. (2024) · doi:10.47952/gro-publ-203
Tracking single units across time using 16-channel microelectrode arrays
Jeakle, E. N., Abbott, J., Smith, T. J., Cogan, S. F., Hernandez-Reynoso, A. G., & Pancrazio, J. J. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10781643
RETRACTED: Monolithic three-dimensional neural probes from deterministic rolling of soft electronicsPreprint
Qiang, Y., & Fang, H. (2025) · Research Square · doi:10.21203/rs.3.rs-6051081/v1
RetractedComparing a BCI communication system in a patient with Multiple System Atrophy, with an animal model
Premchand, B., Toe, K. K., Wang, C., Wan, K. R., Selvaratnam, T., Toh, V. E., Ng, W. H., Libedinsky, C., Chen, W., Lim, R., et al. (2025) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2025.111289
Lehmann, S. J., & Corneil, B. D. (2025) · Brain stimulation · doi:10.1016/j.brs.2025.07.013
A 192-Channel 1D CNN-Based Neural Feature Extractor in 65nm CMOS for Brain-Machine Interfaces
Bulfer, S., Gámez, J., Huang, A. Y., Haghi, B., Pedroni, V. A., Andersen, R. A., & Emami, A. (2025) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2025.3615121
Development of a non-human primate model for preclinical research of a novel auditory nerve implant
Sondh, I., Johnson, L. A., Ghose, G. M., Loveland, A., Larson, L., Lim, H. H., & Adams, M. E. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1669116
Research progress of neural implants: Toward robust brain-computer integration
Chen, Z., Tian, Y., Zeng, M., Wu, X., Wu, C., Wei, D., Sun, J. K., Zhou, L. E. T. A. O., Ding, J., & Fan, H. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.177125
Intracortical BCI Performance is Robust to Changes in Attentional Load During Dual-TaskingPreprint
Canario, E., Shearer, C., Akçakaya, M., Weber, D. J., Chase, S. M., & Collinger, J. L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.16.732398
Self-citationRoberts, S. M., Carter, J. S., Jensen, B. D., Vanfleet, R. R., & Davis, R. C. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1864874
- Authors
- Sponheim, C., Papadourakis, V., Collinger, J. L., Downey, J., Weiss, J., Pentousi, L., Elliott, K., & Hatsopoulos, N. G.
- Affiliations
- University of Chicago (US)
- University of Pittsburgh (US)
- University College London (GB)
- University of Minnesota (US)
- Corresponding author
- Hatsopoulos, Nicholas G.
- ORCID
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2021-12-01
- Volume
- 18
- Issue
- 6
- Pages
- 066044
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- PMC
- PMC8981395
- OpenAlex
- W3215420535
- Cited by
- 66
- References
- 30
- Citations by year
- 2026: 12 · 2025: 24 · 2024: 17 · 2023: 9 · 2022: 4
- FWCI
- 3.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01 NS045853)
- Defense Advanced Research Projects Agency (N66001-10-C-4056)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- Utah electrode array, chronic electrophysiology, signal-to-noise ratio, electrode reliability, platinum metallization, rhesus monkeys, primary motor cortex, prefrontal cortex, somatosensory cortex, premotor cortex
- MeSH terms
- Animals, Electrodes, Implanted, Longevity, Macaca mulatta, Microelectrodes, Utah, Reproducibility of Results
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