Cited by 113Neuroscience and Neural EngineeringEEG and Brain-Computer InterfacesNeural dynamics and brain function

The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.

A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces

Open access

Nason, Samuel R., Vaskov, Alex K., Willsey, Matthew S., Welle, Elissa J., An, Hyochan, Vu, Philip P., Bullard, Autumn J., Nu, Chrono S., Kao, Jonathan C., Shenoy, Krishna V., Jang, Taekwang, Kim, Hun-Seok, Blaauw, David, Patil, Parag G., Chestek, Cynthia A.·2020·Nature Biomedical Engineering

DOI 10.1038/s41551-020-0591-0PMC PMC7982996

Open this paper in CoChat — free account

Abstract

The large power requirement of current brain-machine interfaces is a major hindrance to their clinical translation. In basic behavioural tasks, the downsampled magnitude of the 300-1,000 Hz band of spiking activity can predict movement similarly to the threshold crossing rate (TCR) at 30 kilo-samples per second. However, the relationship between such a spiking-band power (SBP) and neural activity remains unclear, as does the capability of using the SBP to decode complicated behaviour. By using simulations of recordings of neural activity, here we show that the SBP is dominated by local single-unit spikes with spatial specificity comparable to or better than that of the TCR, and that the SBP correlates better with the firing rates of lower signal-to-noise-ratio units than the TCR. With non-human primates, in an online task involving the one-dimensional decoding of the movement of finger groups and in an offline two-dimensional cursor-control task, the SBP performed equally well or better than the TCR. The SBP may enhance the decoding performance of neural interfaces while enabling substantial cuts in power consumption.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/s41551-020-0591-0

Cites

89 of 89 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm
Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., Haddadin, S., Liu, J., Cash, S. S., van der Smagt, P., et al. (2012) · Nature · doi:10.1038/nature11076 · Cited by 2,719
High-performance neuroprosthetic control by an individual with tetraplegia
Collinger, J. L., Wodlinger, B., Downey, J. E., Wang, W., Tyler-Kabara, E. C., Weber, D. J., McMorland, A. J., Velliste, M., Boninger, M. L., & Schwartz, A. B. (2013) · The Lancet · doi:10.1016/s0140-6736(12)61816-9 · Cited by 1,848
A low-power low-noise cmos for amplifier neural recording applications
Harrison, R. R., & Charles, C. (2003) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2003.811979 · Cited by 1,695
Cortical control of a prosthetic arm for self-feeding
Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S., & Schwartz, A. B. (2008) · Nature · doi:10.1038/nature06996 · Cited by 1,591
Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo
Viventi, J., Kim, D.-H., Vigeland, L., Frechette, E. S., Blanco, J. A., Kim, Y.-S., Avrin, A. E., Tiruvadi, V. R., Hwang, S.-W., Vanleer, A. C., et al. (2011) · Nature Neuroscience · doi:10.1038/nn.2973 · Cited by 1,136
Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration
Ajiboye, A. B., Willett, F. R., Young, D. R., Memberg, W. D., Murphy, B. A., Miller, J. P., Walter, B. L., Sweet, J. A., Hoyen, H. A., Keith, M. W., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)30601-3 · Cited by 862
A low-power integrated circuit for a wireless 100-electrode neural recording system.
Harrison, R. R., Watkins, P. T., Kier, R. J., Lovejoy, R. O., Black, D. J., Greger, B., & Solzbacher, F. (2007) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2006.886567 · Cited by 782
A Low-Power Integrated Circuit for a Wireless 100-Electrode Neural Recording System
Harrison, R. R., Watkins, P., Kier, R. J., Lovejoy, R. O., Black, D., Normann, R. A., & Solzbacher, F. (2006) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc.2006.1696288 · Cited by 728
Ultrasmall implantable composite microelectrodes with bioactive surfaces for chronic neural interfaces
Kozai, T. D. Y., Langhals, N. B., Patel, P. R., Deng, X., Zhang, H., Smith, K. L., Lahann, J., Kotov, N. A., & Kipke, D. R. (2012) · Nature Materials · doi:10.1038/nmat3468 · Cited by 721
A brain–spine interface alleviating gait deficits after spinal cord injury in primates
Capogrosso, M., Milekovic, T., Borton, D., Wagner, F., Moraud, E. M., Mignardot, J.-B., Buse, N., Gandar, J., Barraud, Q., Xing, D., et al. (2016) · Nature · doi:10.1038/nature20118 · Cited by 635

Flagged first, then by citations

Show all 88 references

Sorted by citations

The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time–frequency analysis
Arts, L. P. A., & van den Broek, E. L. (2022) · Nature Computational Science · doi:10.1038/s43588-021-00183-z · Cited by 238
Home Use of a Percutaneous Wireless Intracortical Brain-Computer Interface by Individuals With Tetraplegia
Simeral, J. D., Hosman, T., Saab, J., Flesher, S. N., Vilela, M., Franco, B., Kelemen, J. N., Brandman, D. M., Ciancibello, J. G., Rezaii, P. G., et al. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3069119 · Cited by 165
Self-citation
Miniature battery-free bioelectronics
Nair, V., Dalrymple, A. N., Yu, Z., Balakrishnan, G., Bettinger, C. J., Weber, D. J., Yang, K., & Robinson, J. T. (2023) · Science · doi:10.1126/science.abn4732 · Cited by 128
Implantable brain machine interfaces: first-in-human studies, technology challenges and trends
Rapeaux, A. B., & Constandinou, T. G. (2021) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2021.10.001 · Cited by 119
Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus
Wilson, G. H., Stavisky, S. D., Willett, F. R., Avansino, D. T., Kelemen, J. N., Hochberg, L. R., Henderson, J. M., Druckmann, S., & Shenoy, K. V. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abbfef · Cited by 103
Self-citation
Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder
Willsey, M. S., Nason, S. R., Ensel, S., Temmar, H., Mender, M. J., Costello, J. T., Patil, P. G., & Chestek, C. A. (2022) · Nature Communications · doi:10.1038/s41467-022-34452-w · Cited by 104
Self-citation
Multimodal monitoring of human cortical organoids implanted in mice reveal functional connection with visual cortex
Wilson, M. N., Thunemann, M., Liu, X., Lu, Y., Puppo, F., Adams, J. W., Kim, J., Ramezani, M., Pizzo, D. P., Djurovic, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35536-3 · Cited by 110
Challenges and Opportunities of Upconversion Nanoparticles for Emerging NIR Optoelectronic Devices
Geng, S., Li, H., Lv, Z., Zhai, Y., Tian, B., Luo, Y., Zhou, Y., & Han, S. (2025) · Advanced Materials · doi:10.1002/adma.202419678 · Cited by 104
A high-performance brain–computer interface for finger decoding and quadcopter game control in an individual with paralysis
Willsey, M. S., Shah, N. P., Avansino, D. T., Hahn, N. V., Jamiolkowski, R. M., Kamdar, F. B., Hochberg, L. R., Willett, F. R., & Henderson, J. M. (2025) · Nature Medicine · doi:10.1038/s41591-024-03341-8 · Cited by 103
Self-citation
The science and engineering behind sensitized brain-controlled bionic hands
Pandarinath, C., & Bensmaïa, S. J. (2021) · Physiological Reviews · doi:10.1152/physrev.00034.2020 · Cited by 79
Neural Decoding for Intracortical Brain–Computer Interfaces
Dong, Y., Wang, S., Huang, Q., Berg, R. W., Li, G., & He, J. (2023) · Cyborg and Bionic Systems · doi:10.34133/cbsystems.0044 · Cited by 86
High-resolution neural recordings improve the accuracy of speech decoding
Duraivel, S., Rahimpour, S., Chiang, C.-H., Trumpis, M., Wang, C., Barth, K., Harward, S. C., Lad, S. P., Friedman, A. H., Southwell, D. G., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42555-1 · Cited by 75
Long-term performance of Utah slanted electrode arrays and intramuscular electromyographic leads implanted chronically in human arm nerves and muscles
George, J. A., Page, D. M., Davis, T. S., Duncan, C. C., Hutchinson, D. T., Rieth, L. W., & Clark, G. A. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc025 · Cited by 75
Ultra-small carbon fiber electrode recording site optimization and improved in vivo chronic recording yield
Welle, E. J., Patel, P. R., Woods, J. E., Petrossians, A., della Valle, E., Vega-Medina, A., Richie, J. M., Cai, D., Weiland, J. D., & Chestek, C. A. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab8343 · Cited by 68
Self-citation
Closed-loop stimulation using a multiregion brain-machine interface has analgesic effects in rodents
Sun, G., Zeng, F., McCartin, M., Zhang, Q., Xu, H., Liu, Y., Chen, Z. S., & Wang, J. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abm5868 · Cited by 63
2020 International brain–computer interface competition: A review
Jeong, J.-H., Cho, J.-H., Lee, Y.-E., Lee, S., Shin, G.-H., Kweon, Y.-S., Millán, J. D. R., Müller, K., & Lee, S.-W. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.898300 · Cited by 56
Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface
Nason, S. R., Mender, M. J., Vaskov, A. K., Willsey, M. S., Kumar, N. G., Kung, T. A., Patil, P. G., & Chestek, C. A. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.009 · Cited by 55
Self-citation
Inner speech in motor cortex and implications for speech neuroprostheses
Kunz, E. M., Abramovich Krasa, B., Kamdar, F., Avansino, D. T., Hahn, N., Yoon, S., Singh, A., Nason-Tomaszewski, S. R., Card, N. S., Jude, J. J., et al. (2025) · Cell · doi:10.1016/j.cell.2025.06.015 · Cited by 55
“81 For each block, within each electrode, mean threshold crossing rates and spike band power was subtracted from each sample to account for neural nonstationarities (drifts in mean firing rate) which could arise over the course of a session.”
Self-citation
Utah array characterization and histological analysis of a multi-year implant in non-human primate motor and sensory cortices
Patel, P. R., Welle, E. J., Letner, J. G., Shen, H., Bullard, A. J., Caldwell, C. M., Vega-Medina, A., Richie, J. M., Thayer, H. E., Patil, P. G., et al. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acab86 · Cited by 49
Self-citation
Multi-scale neural decoding and analysis
Lu, H., Lorenc, E. S., Zhu, H., Kilmarx, J., Sulzer, J. S., Xie, C., Tobler, P. N., Watrous, A. J., Orsborn, A. L., Lewis‐Peacock, J. A., et al. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac160f · Cited by 47
Neurotechnologies to restore hand functions
Losanno, E., Mender, M. J., Chestek, C. A., Shokur, S., & Micera, S. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00054-4 · Cited by 45
Self-citation
An Ion‐Mediated Spiking Chemical Neuron based on Mott Memristor
Ren, H., Li, F., Wang, M., Liu, G., Li, D., Wang, R., Chen, Y., Tang, Y., Wang, Y., Jin, R., et al. (2024) · Advanced Materials · doi:10.1002/adma.202403678 · Cited by 41
Brain–Machine Interfaces: Closed-Loop Control in an Adaptive System
Sorrell, E., Rule, M. E., & O’Leary, T. (2021) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-061720-012348 · Cited by 41
Brain–computer interfaces for neuropsychiatric disorders
Oganesian, L. L., & Shanechi, M. M. (2024) · Nature Reviews Bioengineering · doi:10.1038/s44222-024-00177-2 · Cited by 47
Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks
Sani, O. G., Pesaran, B., & Shanechi, M. M. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01731-2 · Cited by 65
A Light-Tolerant Wireless Neural Recording IC for Motor Prediction With Near-Infrared-Based Power and Data Telemetry
Lim, J., Lee, J., Moon, E., Barrow, M., Atzeni, G., Letner, J. G., Costello, J. T., Nason, S. R., Patel, P. R., Sun, Y., et al. (2022) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2022.3141688 · Cited by 38
Self-citation
BRAND: a platform for closed-loop experiments with deep network models
Ali, Y. H., Bodkin, K., Rigotti-Thompson, M., Patel, K., Card, N. S., Bhaduri, B., Nason-Tomaszewski, S. R., Mifsud, D. M., Hou, X., Nicolas, C., et al. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad3b3a · Cited by 32
Neural Plasticity in Sensorimotor Brain–Machine Interfaces
Dadarlat, M. C., Canfield, R. A., & Orsborn, A. L. (2023) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-110220-110833 · Cited by 35
A Power-Efficient Brain-Machine Interface System With a Sub-mw Feature Extraction and Decoding ASIC Demonstrated in Nonhuman Primates
An, H., Nason, S. R., Lim, J., Kwon, K., Willsey, M. S., Patil, P. G., Kim, H.-S., Sylvester, D., Chestek, C. A., & Blaauw, D. T. (2022) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2022.3175926 · Cited by 33
Self-citation
Bridging the “last millimeter” gap of brain-machine interfaces via near-infrared wireless power transfer and data communications
Moon, E., Barrow, M., Lim, J., Lee, J., Nason, S. R., Costello, J., Kim, H. S., Chestek, C., Jang, T., Blaauw, D., et al. (2021) · ACS Photonics · doi:10.1021/acsphotonics.1c00160 · Cited by 31
Self-citation
Enhanced control of a brain–computer interface by tetraplegic participants via neural-network-mediated feature extraction
Haghi, B., Aflalo, T., Kellis, S., Guan, C., Gámez, J., Huang, A. Y., Pouratian, N., Andersen, R. A., & Emami, A. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01297-1 · Cited by 36
Two-dimensional materials for wireless power transfer
Gao, M., Yao, Y., Yang, F., Ye, J., Liu, G., Wang, B., Liu, S., Wang, P., & Lu, Y. (2023) · Device · doi:10.1016/j.device.2023.100022 · Cited by 32
Large-scale, closed-loop interrogation of neural circuits underlying cognition.
Khodagholy, D., Ferrero, J. J., Park, J., Zhao, Z., & Gelinas, J. N. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.10.003 · Cited by 27
Stretchable Surface Electrode Arrays Using an Alginate/PEDOT:PSS-Based Conductive Hydrogel for Conformal Brain Interfacing
Lee, S., Park, K., Kum, J., An, S., Yu, K. J., Kim, H., Shin, M., & Son, D. (2022) · Polymers · doi:10.3390/polym15010084 · Cited by 25
Self-citation
A 1024-Channel 268-nW/Pixel 36×36 μm 2 /Channel Data-Compressive Neural Recording IC for High-Bandwidth Brain–Computer Interfaces
Jang, M., Hays, M., Yu, W.-H., Lee, C., Caragiulo, P., Ramkaj, A. T., Wang, P., Phillips, A., Vitale, N. H., Tandon, P., et al. (2023) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2023.3344798 · Cited by 23
A 32-Channel Time-Multiplexed Artifact-Aware Neural Recording System
Pérez-Prieto, N., Rodriguez-Vazquez, A., Álvarez‐Dolado, M., & Delgado‐Restituto, M. (2021) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2021.3108725 · Cited by 23
An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interface
Liao, J., Widmer, L., Wang, X., Mauro, A. D., Nason, S. R., Chestek, C. A., Luca Benini, C., & Jang, T. (2022) · IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas54282.2022.9869846 · Cited by 22
Self-citation
Comparative analysis of energy transfer mechanisms for neural implants
Miziev, S., Pawlak, W. A., & Howard, N. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1320441 · Cited by 21
Machine learning algorithm for decoding multiple subthalamic spike trains for speech brain-machine interfaces
Tankus, A., Solomon, L., Aharony, Y., Faust-Socher, A., & Strauss, I. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac3315 · Cited by 20
Closed-loop sacral neuromodulation for bladder function using dorsal root ganglia sensory feedback in an anesthetized feline model
Ouyang, Z., Barrera, N. D., Sperry, Z. J., Bottorff, E. C., Bittner, K. C., Zirpel, L., & Bruns, T. M. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02554-8 · Cited by 21
A 2.46-mm2 Miniaturized Brain-Machine Interface (MiBMI) Enabling 31-Class Brain-to-Text Decoding
Shaeri, M., Shin, U., Yadav, A., Caramellino, R., Rainer, G., & Shoaran, M. (2024) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2024.3443254 · Cited by 19
Data Transformation in the Processing of Neuronal Signals: A Powerful Tool to Illuminate Informative Contents
Shaeri, M., & Sodagar, A. M. (2022) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2022.3151340 · Cited by 18
Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs
Shaeri, M., Afzal, A., & Shoaran, M. (2022) · IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas54282.2022.9870008 · Cited by 19
Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces
Hueber, P., Tang, G., Sifalakis, M., Liaw, H.-P., Micheli, A., Tömen, N., & Liu, Y. (2024) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ad4411 · Cited by 18
Post-explant profiling of subcellular-scale carbon fiber intracortical electrodes and surrounding neurons enables modeling of recorded electrophysiology
Letner, 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. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acbf78 · Cited by 16
Self-citation
Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials
Cao, L., Varga, V., & Chen, Z. S. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100101 · Cited by 16
High-density neural recording system design
Lee, H.-S., Eom, K., Park, M., Ku, S.-B., Lee, K., & Lee, H.-M. (2022) · Biomedical Engineering Letters · doi:10.1007/s13534-022-00233-z · Cited by 17
A low-power communication scheme for wireless, 1000 channel brain–machine interfaces
Costello, J. T., Nason, S. R., An, H., Lee, J., Mender, M. J., Temmar, H., Wallace, D. M., Lim, J., Willsey, M. S., Patil, P. G., et al. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac7352 · Cited by 14
Self-citation
An Ultra-Low-Noise, Low Power and Miniaturized Dual-Channel Wireless Neural Recording Microsystem
Wang, H., Ma, Q., Chen, K., Zhang, H., Yang, Y., Zheng, N., & Hong, H. (2022) · Biosensors · doi:10.3390/bios12080613 · Cited by 12
Flexible and smart electronics for single-cell resolved brain–machine interfaces
Lee, A. J., Wang, W., & Jia Liu, B. (2023) · Applied Physics Reviews · doi:10.1063/5.0115879 · Cited by 11
Reconstructing Multi-Stroke Characters From Brain Signals Toward Generalizable Handwriting Brain–Computer Interfaces
Yang, X., Xiong, X., Li, X., Lian, Q., Zhu, J., Zhang, J., Qi, Y., & Wang, Y. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3492191 · Cited by 12
Firing activities induced by various stimuli in a memristive ion channel-based bionic circuit
Ding, X., Feng, C., Wang, N., Wu, H., & Xu, Q. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.116587 · Cited by 12
Balancing memorization and generalization in RNNs for high performance brain-machine Interfaces
Costello, J. T., Temmar, H., Cubillos, L. H., Mender, M. J., Wallace, D. M., Willsey, M. S., Patil, P. G., & Chestek, C. A. (2023) · neural information processing systems · doi:10.52202/075280-0328 · Cited by 11
Self-citation
Decoding pain from brain activity
Chen, Z. S. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac28d4 · Cited by 12
Machine learning-based high-frequency neuronal spike reconstruction from low-frequency and low-sampling-rate recordings
Hong, N., Kim, B., Lee, J., Choe, H. K., Jin, K. H., & Kang, H. (2024) · Nature Communications · doi:10.1038/s41467-024-44794-2 · Cited by 12
Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participantsPreprint
Hahn, N. V., Stein, E. A., Donoghue, J. P., Simeral, J. D., Hochberg, L. R., & Willett, F. R. (2025) · medRxiv · doi:10.1101/2025.07.02.25330310 · Cited by 15
Technical survey of end-to-end signal processing in BCIs using invasive MEAs
Erbslöh, A., Buron, L., Ur-Rehman, Z., Musall, S., Hrycak, C., Löhler, P., Klaes, C., Seidl, K., & Schiele, G. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad8031 · Cited by 11
Unsupervised learning of stationary and switching dynamical system models from Poisson observations
Song, C. Y., & Shanechi, M. M. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad038d · Cited by 10
Real-time, neural signal processing for high-density brain-implantable devices
Sodagar, A. M., Khazaei, Y., Nekoui, M., & Shaeri, M. (2025) · Bioelectronic Medicine · doi:10.1186/s42234-025-00177-6 · Cited by 10
Machine learning decoding of single neurons in the thalamus for speech brain-machine interfaces
Tankus, A., Rosenberg, N., Ben-Hamo, O., Stern, E., & Strauss, I. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad4179 · Cited by 9
Decoding Joint-Level Hand Movements With Intracortical Neural Signals in a Human Brain–Computer Interface
Sun, H., Qi, Y., Wu, X., Zhu, J., Zhang, J., & Wang, Y. (2024) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2024.3409555 · Cited by 8
Restoring continuous finger function with temporarily paralyzed nonhuman primates using brain–machine interfaces
Nason, S. R., Mender, M. J., Kennedy, E., Lambrecht, J. M., Kilgore, K. L., Chiravuri, S., Kumar, N. G., Kung, T. A., Willsey, M. S., Chestek, C. A., et al. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/accf36 · Cited by 10
Self-citation
Error detection and correction in intracortical brain–machine interfaces controlling two finger groups
Wallace, D. M., Benyamini, M., Nason, S. R., Costello, J. T., Cubillos, L. H., Mender, M. J., Temmar, H., Willsey, M. S., Patil, P. G., Chestek, C. A., et al. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acef95 · Cited by 7
Self-citation
Event detection and classification from multimodal time series with application to neural data
Sadras, N., Pesaran, B., & Shanechi, M. M. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad3678 · Cited by 7
The impact of task context on predicting finger movements in a brain-machine interface
Mender, M. J., Nason, S. R., Temmar, H., Costello, J. T., Wallace, D. M., Willsey, M. S., Kumar, N. G., Kung, T. A., Patil, P. G., & Chestek, C. A. (2023) · eLife · doi:10.7554/elife.82598 · Cited by 7
Self-citation
Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity
Ahmadipour, P., Sani, O. G., Pesaran, B., & Shanechi, M. M. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad1053 · Cited by 7
Optimization Driven Spike Deep Belief Neural Network classifier: a deep-learning based Multichannel Spike Sorting Neural Signal Processor (NSP) module for high-channel-count Brain Machine Interfaces (BMIs)
Reddy, V. K., Melingi, S. B., Kumar, C. V. M. S. N. P., Kumar, K. A., & Mojjada, R. K. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10575-4 · Cited by 6
Investigating the benefits of artificial neural networks over linear approaches to BMI decoding
Temmar, H., Willsey, M. S., Costello, J. T., Mender, M. J., Cubillos, L. H., DeMatteo, J. C., Lam, J. L., Wallace, D. M., Kelberman, M. M., Patil, P. G., et al. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/ade568 · Cited by 11
Self-citation
Decoding Handwriting Trajectories from Intracortical Brain Signals for Brain‐to‐Text Communication
Xu, G., Wang, Z., Xu, K., Zhu, J., Zhang, J., Wang, Y., & Hao, Y. (2025) · Advanced Science · doi:10.1002/advs.202505492 · Cited by 9
A brain machine interface framework for exploring proactive control of smart environments
Braun, J.-M., Fauth, M. J., Berger, M., Huang, N.-S., Simeoni, E., Gaeta, E., do Carmo, R. R., García-Betances, R. I., Arredondo, M. T., Gail, A., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-60280-7 · Cited by 7
A machine learning approach to characterize sequential movement-related states in premotor and motor cortices
DePass, M., Falaki, A., Quessy, S., Dancause, N., & Cos, I. (2022) · Journal of Neurophysiology · doi:10.1152/jn.00368.2021 · Cited by 7
Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals
Lin, J., Lai, D., Wan, Z., Feng, L., Zhu, J., Zhang, J., Wang, Y., & Xu, K. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1168017 · Cited by 5
Progressive Retinal Degeneration Increases Cortical Response Latency of Light Stimulation but Not of Electric Stimulation
Koo, B., & Weiland, J. D. (2022) · Translational Vision Science & Technology · doi:10.1167/tvst.11.4.19 · Cited by 5
Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methodsPreprint
Temmar, H., Willsey, M. S., Costello, J. T., Mender, M. J., Cubillos, L. H., Lam, J. L., Wallace, D. M., Kelberman, M. M., Patil, P. G., & Chestek, C. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.01.583000 · Cited by 5
Self-citation
NeuroGator: A Low-Power Gating System for Asynchronous BCI Based on LFP Brain State Estimation
He, B., Liu, C., Qi, Z., Xue, N., & Yao, L. (2026) · Brain Sciences · doi:10.3390/brainsci16020141 · Cited by 3
Brain-Machine Interfaces
Vaskov, A. K., & Chestek, C. A. (2021) · Hand Clinics · doi:10.1016/j.hcl.2021.04.003 · Cited by 3
Self-citation
Challenges in neural interface electronics: miniaturization and wireless operation
Mutlu, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822828-9.00001-0 · Cited by 3
Multimodal monitoring of human cortical organoids implanted in mice using transparent graphene microelectrodes reveal functional connection between organoid and mouse visual cortexPreprint
Wilson, M. N., Thunemann, M., Liu, X., Lu, Y., Puppo, F., Adams, J. W., Kim, J., Pizzo, D. P., Djurovic, S., Andreassen, O. A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.16.496469 · Cited by 3
Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces
Cubillos, L. H., Revach, G., Mender, M. J., Costello, J., Temmar, H., Hite, A., Zutshi, D., Wallace, D. M., Ni, X., Kelberman, M. M., et al. (2024) · neural information processing systems · doi:10.52202/079017-4257 · Cited by 6
Self-citation
An Efficient Brain-Switch for Asynchronous Brain-Computer Interfaces
Valencia, D., Mercier, P. P., & Alimohammad, A. (2024) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2024.3396115 · Cited by 3
Cortical Response to Expectation of Tactile Stimulation from External Anthropomorphic and Non-Anthropomorphic Systems
Osborn, L. E., Christie, B. P., Crego, A. C. G., D'almeida, D., McMullen, D. P., Nickl, R. W., Pawar, A. S., Brown, J. D., Wester, B. A., Firestone, C., et al. (2023) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner52421.2023.10123891 · Cited by 2
Increasing power efficiency
Slutzky, M. W. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00631-7 · Cited by 2
Decoding imaginary handwriting trajectories with shape and time distortion loss for brain-to-text communicationPreprint
Xu, G., Wang, Z., Xu, K., Zhu, J., Zhang, J., Wang, Y., & Hao, Y. (2024) · medRxiv · doi:10.1101/2024.07.02.24309802 · Cited by 2
Recording Front-End Electronics for Large-Scale Implantable Brain-Computer Interfaces: A Design Perspective
Huang, X., & Muratore, D. G. (2025) · Custom Integrated Circuits Conference · doi:10.1109/cicc63670.2025.10983696 · Cited by 2
State-Dependent Neural Decoding of Complex Handwriting via Mixture of Experts With Recurrent Routing
Wang, L., Yang, X., Xiong, X., Jia, Y., Wang, Y., & Qi, Y. (2025) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2025.3642409 · Cited by 1
Restoring upper extremity function with brain-machine interfaces
Nason, S. R., Mender, M. J., Letner, J. G., Chestek, C. A., & Patil, P. G. (2021) · International review of neurobiology · doi:10.1016/bs.irn.2021.06.001 · Cited by 2
Self-citation
The impact of reducing signal acquisition specifications on neuronal spike sorting
Hermiz, J., Joseph, E., Lee, K. H., Baldacci, I. A., Chung, J. E., Frank, L. M., Bouchard, K. E., & Denes, P. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630669 · Cited by 1
Advancing Brain-Machine Interfaces: High Data Rate Battery-Free Implants
Hasanvand, A., Khaleghi, A., Balasingham, I., Wanda, P. A., & Beguet, C. (2023) · IEEE MTT-S International Microwave Biomedical Conference (IMBioC) · doi:10.1109/imbioc56839.2023.10305091 · Cited by 1
Reducing power requirements for high-accuracy decoding in iBCIs
Karpowicz, B. M., Bhaduri, B., Nason, S. R., Jacques, B. G., Ali, Y. H., Flint, R. D., Bechefsky, P. H., Hochberg, L. R., Yong, N. A., Slutzky, M. W., et al. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad88a4 · Cited by 1
Self-citation
Brain-Controlled Electrical Stimulation Restores Continuous Finger FunctionPreprint
Nason, S. R., Mender, M. J., Kennedy, E., Lambrecht, J. M., Kilgore, K. L., Chiravuri, S., Kumar, N. G., Kung, T. A., Willsey, M. S., Chestek, C. A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.15.496349 · Cited by 1
Self-citation
Sparse neural networks enable low-power, implantable neural interfacesPreprint
Costello, J. T., Temmar, H., Cubillos, L. H., Mender, M. J., Wallace, D. M., Kelberman, M. M., Benharush, O., Simons, J., Willsey, M. S., Kumar, N. G., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.17.628936 · Cited by 1
Self-citation
Closed-loop Control of Sacral Neuromodulation for Bladder Function using Dorsal Root Ganglia Sensory Feedback in an Acute Feline ModelPreprint
Bruns, T. M., & Ouyang, Z. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.17605/osf.io/3bkq9
Unsupervised Channel Compression Methods in Motor Prostheses Design
Alothman, A., & Gilja, V. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630343
PROGRESS OF ORGANISMS HEALTH & INTELLIGENCE VIA NANITES & REAL-TIME MONITORING OF THEM VIA WIRELESS NETWORKS & MECHATRONIC SYSTEMSPreprint
Chaideftos, C. (2020) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/hv5cx
Profiling neurons surrounding subcellular-scale carbon fiber electrode tracts enables modeling recorded signalsPreprint
Letner, 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
Self-citation
Harnessing Neurotechnology: Advancements and applications of brain implant
Divya, B., Maheshwari, K., Balakoti, J., Pravalika, P., Shirisha, G., & Sharma, J. (2024) · International Journal of Pharmaceutical Sciences and Drug Analysis · doi:10.22271/27889246.2024.v4.i1a.78
Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance
Cubillos, L. H., Kelberman, M. M., Mender, M. J., Hite, A., Temmar, H., Willsey, M. S., Kumar, N. G., Kung, T. A., Patil, P. G., Chestek, C. A., et al. (2026) · Restorative Neurology and Neuroscience · doi:10.1177/09226028261457824
Self-citation
MEMS-based High-Density Ultra-Conformal μECOG Electrode Array for Real-Time Motor Decoding
Zhou, E., Liu, C., Wang, X., Wei, X., Sun, L., Tao, T. H., & Zhou, Z. (2025) · IEEE International Conference on Micro Electro Mechanical Systems (MEMS) · doi:10.1109/mems61431.2025.10918073
A Novel Soul Splitting Model in Higher Dimensions by Cracking Tesla's "369 Code": Interrelation of Science, Numerology & SpiritualityPreprint
Mukherjee, I. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5217757
Prioritized learning of cross-population neural dynamics
Jha, T., Sani, O. G., Pesaran, B., & Shanechi, M. M. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/ade569 · Cited by 3

Authors: Nason, S. R., Vaskov, A. K., Willsey, M. S., Welle, E. J., An, H., Vu, P. P., Bullard, A. J., Nu, C. S., Kao, J. C., Shenoy, K. V., Jang, T., Kim, H.-S., Blaauw, D., Patil, P. G., & Chestek, C. A.
Affiliations: University of Michigan (US)
University of California, Los Angeles (US)
Howard Hughes Medical Institute (US)
Corresponding author: Chestek, Cynthia A.
ORCID: Nason, Samuel R.
Vaskov, Alex K.
Willsey, Matthew S.
Welle, Elissa J.
An, Hyochan
Vu, Philip P.
Bullard, Autumn J.
Kao, Jonathan C.
Shenoy, Krishna V.
Jang, Taekwang
Kim, Hun-Seok
Blaauw, David
Patil, Parag G.
Chestek, Cynthia A.
Venue: Nature Biomedical Engineering
Publisher: Springer Science and Business Media LLC
Published: 2020-07-27
Volume: 4
Issue: 10
Pages: 973-983
ISSN: 2157-846X (ISSN-L)
DOI: 10.1038/s41551-020-0591-0
PMC: PMC7982996
OpenAlex: W3044236731
Cited by: 113
References: 88
Citations by year: 2026: 7 · 2025: 21 · 2024: 22 · 2023: 19 · 2022: 22 · 2021: 16 · 2020: 5
FWCI: 7.10
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Biomedical Engineering (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: U.S. Department of Health & Human Services | National Institutes of Health (T32NS007222, U01NS094375, UF1NS107659, R21EY029452, R01NS076460, R01MH09964703, 8DP1HD075623, R01GM111293)
U.S. Department of Health & Human Services | NIH | NIH Office of the Director (OT2OD024907)
U-M | MCubed, University of Michigan (1482)
National Science Foundation (Graduate Research Fellowships Program, 1926576)
United States Department of Defense | Defense Advanced Research Projects Agency (N66001-10-C-2010, W911NF-14-2-0013)
Simons Foundation (543045)
United States Department of Defense | United States Navy | Office of Naval Research (W911NF-14-2-0013)
Howard Hughes Medical Institute
A. Alfred Taubman Medical Research Institute
Craig H. Neilsen Foundation (315108)
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: brain-computer interface, spiking activity, single-unit activity, neural decoding, signal-to-noise ratio, non-human primates, cursor control, motion prediction, power consumption
MeSH terms: Animals, Fingers, Macaca mulatta, Male, Microelectrodes, Motor Cortex, Neurons, Prostheses and Implants, Rats, Long-Evans, Signal-To-Noise Ratio, Brain-Computer Interfaces

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks