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Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortex
Abstract
Multiple-electrode arrays are valuable both as a research tool and as a sensor for neuromotor prosthetic devices, which could potentially restore voluntary motion and functional independence to paralyzed humans. Long-term array reliability is an important requirement for these applications. Here, we demonstrate the reliability of a regular array of 100 microelectrodes to obtain neural recordings from primary motor cortex (MI) of monkeys for at least three months and up to 1.5 years. We implanted Bionic (Cyberkinetics, Inc., Foxboro, MA) silicon probe arrays in MI of three Macaque monkeys. Neural signals were recorded during performance of an eight-direction, push-button task. Recording reliability was evaluated for 18, 35, or 51 sessions distributed over 83, 179, and 569 days after implantation, respectively, using qualitative and quantitative measures. A four-point signal quality scale was defined based on the waveform amplitude relative to noise. A single observer applied this scale to score signal quality for each electrode. A mean of 120 (+/- 17.6 SD), 146 (+/- 7.3), and 119 (+/- 16.9) neural-like waveforms were observed from 65-85 electrodes across subjects for all recording sessions of which over 80% were of high quality. Quantitative measures demonstrated that waveforms had signal-to-noise ratio (SNR) up to 20 with maximum peak-to-peak amplitude of over 1200 microv with a mean SNR of 4.8 for signals ranked as high quality. Mean signal quality did not change over the duration of the evaluation period (slope 0.001, 0.0068 and 0.03; NS). By contrast, neural waveform shape varied between, but not within days in all animals, suggesting a shifting population of recorded neurons over time. Arm-movement related modulation was common and 66% of all recorded neurons were tuned to reach direction. The ability for the array to record neural signals from parietal cortex was also established. These results demonstrate that neural recordings that can provide movement related signals for neural prostheses, as well as for fundamental research applications, can be reliably obtained for long time periods using a monolithic microelectrode array in primate MI and potentially from other cortical areas as well.
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“In fact, after appropriate control in experimental models to test their biocompatibility and efficacy [18], LIFEs have been recently used to control artificial devices [6,7,22-25] showing good results during short-term trials with human amputees.”
From Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces (2011) · cites this paper as [22]
In the citing paper
“These trials showed that subjects are able to control a one-degree of freedom hand prosthesis through online processing of the efferent neural signals, and that repeatable sensory feedback may be received by stimulating afferent fibers [22,23] corresponding to the missing hand/fingers territories.”
From Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces (2011) · cites this paper as [22]
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Spike Train SIMilarity Space (SSIMS): A Framework for Single Neuron and Ensemble Data Analysis
Vargas-Irwin, C. E., Brandman, D. M., Zimmermann, J. B., Donoghue, J. P., & Black, M. J. (2014) · Neural Computation · doi:10.1162/neco_a_00684 · Cited by 44
Self-citationXu, H., Weltman, A., Scholten, K., Berger, T. W., Song, D. B., & Meng, E. F. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa9451 · Cited by 48
Mental Workload Estimation Based on Physiological Features for Pilot-UAV Teaming Applications
Singh, G., Chanel, C. P. C., & Roy, R. N. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.692878 · Cited by 45
Samonds, J. M., Potetz, B. R., & Lee, T. S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1200125109 · Cited by 44
“First, we recorded from neurons using a 10 × 10 Utah Intracortical Array (400-μm spacing), using methods described previously (50, 51).”
BackgroundMethodDecoding Movement Intent From Human Premotor Cortex Neurons for Neural Prosthetic Applications
Ojakangas, C. L., Shaikhouni, A., Friehs, G. M., Caplan, A. H., Serruya, M. D., Saleh, M., Morris, D. S., & Donoghue, J. P. (2006) · Journal of Clinical Neurophysiology · doi:10.1097/01.wnp.0000233323.87127.14 · Cited by 44
“Chronically implanted arrays capable of recording dozens of cells (Paninski et al., 2004) have been tested in monkeys (Serruya et al., 2002; Suner et al., 2005) and in a human (Mukand et al., 2004).”
BackgroundSelf-citationSharma, G., Annetta, N., Friedenberg, D., Blanco, T., Vasconcelos, D., Shaikhouni, A., Rezai, A. R., & Bouton, C. (2015) · Bioelectronic Medicine · doi:10.15424/bioelectronmed.2015.00010 · Cited by 41
Vargas-Irwin, C. E., Franquemont, L., Black, M. J., & Donoghue, J. P. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1574-15.2015 · Cited by 44
“Each electrode array contained a 4 4 mm grid with 96 active silicon-based electrodes 1 mm in length and spaced 400 m apart (for details on array structure and surgical procedures, see Suner et al., 2005; Barrese et al., 2013).”
MethodSelf-citationKoch, J., Schüettler, M., Pasluosta, C. F., & Stieglitz, T. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab36df · Cited by 44
Local and Global Correlations between Neurons in the Middle Temporal Area of Primate Visual Cortex
Solomon, S. S., Chen Spencer, C., Morley, J. W., & Solomon, S. G. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu111 · Cited by 42
Robust penetrating microelectrodes for neural interfaces realized by titanium micromachining
McCarthy, P. T., Otto, K. J., & Rao, M. P. (2011) · Biomedical Microdevices · doi:10.1007/s10544-011-9519-5 · Cited by 50
“The signal-tonoise ratio was 6.64, which is comparable to “good” recordings from conventional silicon-based microelectrode arrays (Suner et al. 2005).”
BackgroundMethodInfluentialCharacterizing neural dependencies with copula models
Berkes, P., Wood, F., & Pillow, J. W. (2008) · Cited by 41
Fernández, E., Alfaro, A., & González-López, P. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00681 · Cited by 35
Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling
Carlson, D., Vogelstein, J. T., Wu, Q., Lian, W., Zhou, M., Stoetzner, C. R., Kipke, D. R., Weber, D. J., Dunson, D. B., & Carin, L. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2275751 · Cited by 42
Tankus, A., Yeshurun, Y., & Fried, I. (2009) · Journal of Neural Engineering · doi:10.1088/1741-2560/6/5/056001 · Cited by 41
“Human classifiers rely on visual inspection as their main evaluation tool (see for example Vargas-Irwin and Donoghue (2007), Rutishauser et al (2006), Tankus et al (2009), Stark and Abeles (2007), Suner et al (2005), Kim et al (2008)).”
MethodKim, T., Branner, A., Gulati, T., & Giszter, S. F. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/4/045001 · Cited by 39
“To calculate the signal-tonoise ratio (SNR), we used the following equation described by Suner et al [36]:”
Cue to action processing in motor cortex populations
Rao, N. G., & Donoghue, J. P. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00274.2013 · Cited by 42
Self-citationResearch Progress on the Flexibility of an Implantable Neural Microelectrode
Zhao, H., Liu, R., Zhang, H., Cao, P., Liu, Z., & Li, Y. (2022) · Micromachines · doi:10.3390/mi13030386 · Cited by 37
Temporal Processing in the Visual Cortex of the Awake and Anesthetized Rat
Aasebø, I. E. J., Lepperød, M. E., Stavrinou, M., Nøkkevangen, S., Einevoll, G. T., Hafting, T., & Fyhn, M. (2017) · eNeuro · doi:10.1523/eneuro.0059-17.2017 · Cited by 38
“To ensure that other cell classes did not interfere with the classification of units, the following units were excluded: units that reached signal saturation, that had a low signal-to-noise ratio (SNR 2.75; Suner et al., 2005; Smith and Kohn, 2008), or units that showed the triphasic wave form typically associated wit…”
BackgroundInfluentialCaldwell, R., Mandal, H. S., Sharma, R., Solzbacher, F., Tathireddy, P., & Rieth, L. W. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa69d3 · Cited by 36
Neural electrodes for brain‐computer interface system: From rigid to soft
Yang, D., Tian, G., Chen, J., Liu, Y., Fatima, E., Qiu, J., Malek, N. A. N. N., & Qi, D. (2025) · BMEMat · doi:10.1002/bmm2.12130 · Cited by 37
An Implantable Neural Sensing Microsystem with Fiber-Optic Data Transmission and Power Delivery
Park, S., Borton, D. A., Kang, M., Nurmikko, A. V., & Song, Y. (2013) · Sensors · doi:10.3390/s130506014 · Cited by 34
“For example, chronically implanted, multielectrode arrays have allowed long term (>1 year) exploration of reach and grasp representation in the motor cortex of rhesus macaques [4].”
BackgroundAssessment of gliosis around moveable implants in the brain
Stice, P., & Muthuswamy, J. (2009) · Journal of Neural Engineering · doi:10.1088/1741-2560/6/4/046004 · Cited by 34
Semiconducting electrodes for neural interfacing: a review
Ahnood, A., Chambers, A., Gelmi, A. A., Yong, K., & Kavehei, O. (2023) · Chemical Society Reviews · doi:10.1039/d2cs00830k · Cited by 33
Magnetically Inserted Neural Electrodes: Tissue Response and Functional Lifetime
Dryg, I., Ward, M. P., Qing, K. Y., Mei, H., Schaffer, J. E., & Irazoqui, P. P. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2399856 · Cited by 33
“Spike sorting was performed using Osort [28], and signal-to-noise ratio (SNR) was calculated as previously described [29]”
MethodHu, Y., Yang, G., Liang, B., Lü, F., Ma, G., Zhu, Q. Y., Chen, S., & Ye, X. (2014) · Acta Biomaterialia · doi:10.1016/j.actbio.2014.11.023 · Cited by 32
Intracortical Microelectrode Array Unit Yield under Chronic Conditions: A Comparative Evaluation
Usoro, J. O., Sturgill, B. S., Musselman, K. C., Capadona, J. R., & Pancrazio, J. J. (2021) · Micromachines · doi:10.3390/mi12080972 · Cited by 34
An Ultraflexible Electrode Array for Large‐Scale Chronic Recording in the Nonhuman Primate Brain
Tian, Y., Yin, J., Wang, C., He, Z., Xie, J., Feng, X., Zhou, Y., Ma, T., Xie, Y., Li, X., et al. (2023) · Advanced Science · doi:10.1002/advs.202302333 · Cited by 32
Frampton, J. P., Hynd, M. R., Williams, J. C., Shuler, M. L., & Shain, W. G. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/4/006 · Cited by 31
Carbon‐Nanotube‐Modified Electrodes for Highly Efficient Acute Neural Recording
Shin, J. H., Kim, G. B., Lee, E. J., An, T., Shin, K., Lee, S. E., Choi, W., Lee, S., Latchoumane, C.-F. V., Shin, H., et al. (2013) · Advanced Healthcare Materials · doi:10.1002/adhm.201300183 · Cited by 30
Bjånes, D. A., Kellis, S., Nickl, R., Baker, B., Aflalo, T., Bashford, L., Chivukula, S., Fifer, M. S., Osborn, L. E., Christie, B., et al. (2025) · Acta Biomaterialia · doi:10.1016/j.actbio.2025.02.030 · Cited by 37
“However, longitudinal BCI performance from intracortical microelectrodes (such as the “ Utah ” array) varies widely between participants, as recording quality deteriorates over time [23, 25 – 28].”
Neural Recording Stability of Chronic Electrode Arrays in Freely Behaving Primates
Linderman, M. D., Gilja, V., Santhanam, G., Afshar, A., Ryu, S. I., Meng, T. H. Y., & Shenoy, K. V. (2006) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2006.260814 · Cited by 29
“The HermesB system, a new autonomous, long-duration neural recording system for freely behaving non-human primates, enables collection of the previously unavailable multi-day broadband datasets needed for this characterization [2].”
BackgroundElectrothermal Microactuators With Peg Drive Improve Performance for Brain Implant Applications
Anand, S., Sutanto, J., Baker, M. S., Okandan, M., & Muthuswamy, J. (2012) · Journal of Microelectromechanical Systems · doi:10.1109/jmems.2012.2203789 · Cited by 28
Paralikar, K. J., Rao, C. R., & Clement, R. S. (2009) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2009.04.014 · Cited by 28
“Neural spiking events are typically detected using a pre-defined threshold on the recorded electrode followed by template-matching, Principal Component Analysis(PCA), clustering techniques, or Bayesian filters (Chapin et al., 1999; Isaacs et al., 2000; Lewicki, 1998; Rennaker et al., 2005; Suner et al., 2005) to selec…”
Method
Metadata
- Authors
- Suner, S., Fellows, M. R., Vargas-Irwin, C., Nakata, G. K., & Donoghue, J. P.
- Affiliations
- Brown University (US)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2005-12
- Volume
- 13
- Issue
- 4
- Pages
- 524-541
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2113853842
- Cited by
- 415
- References
- 30
- Citations by year
- 2026: 6 · 2025: 17 · 2024: 12 · 2023: 13 · 2022: 13 · 2021: 25 · 2020: 10 · 2019: 15 · 2018: 25 · 2017: 23 · 2016: 19 · 2015: 28 · 2014: 36 · 2013: 26 · 2012: 16
- FWCI
- 6.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (NS-25074)
- 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
- chronic neural recording, primary motor cortex, non-human primates, macaque, signal-to-noise ratio, neuroprosthetics, parietal cortex, long-term implantation
- MeSH terms
- Action Potentials, Animals, Electrodes, Implanted, Electroencephalography, Equipment Design, Female, Humans, Macaca mulatta, Male, Microelectrodes, Motor Cortex, Primates, Sensitivity and Specificity, Silicon, Reproducibility of Results, Evoked Potentials, Motor, Equipment Failure Analysis
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