The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.
Active tactile exploration using a brain–machine–brain interface
Open accessAbstract
Brain-machine interfaces use neuronal activity recorded from the brain to establish direct communication with external actuators, such as prosthetic arms. It is hoped that brain-machine interfaces can be used to restore the normal sensorimotor functions of the limbs, but so far they have lacked tactile sensation. Here we report the operation of a brain-machine-brain interface (BMBI) that both controls the exploratory reaching movements of an actuator and allows signalling of artificial tactile feedback through intracortical microstimulation (ICMS) of the primary somatosensory cortex. Monkeys performed an active exploration task in which an actuator (a computer cursor or a virtual-reality arm) was moved using a BMBI that derived motor commands from neuronal ensemble activity recorded in the primary motor cortex. ICMS feedback occurred whenever the actuator touched virtual objects. Temporal patterns of ICMS encoded the artificial tactile properties of each object. Neuronal recordings and ICMS epochs were temporally multiplexed to avoid interference. Two monkeys operated this BMBI to search for and distinguish one of three visually identical objects, using the virtual-reality arm to identify the unique artificial texture associated with each. These results suggest that clinical motor neuroprostheses might benefit from the addition of ICMS feedback to generate artificial somatic perceptions associated with mechanical, robotic or even virtual prostheses.
Overview
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
Cites
Statistics
Verification
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Motor intention originates from the primary motor cortex of the brain and is transmitted from the central nervous system to the peripheral nervous system in the form of electrophysiological signals, which are translated into mechanical contractions of the muscles.[269]”
From Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics (2022) · cites this paper as [269]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“There are currently two different types of BMI systems. One type samples the neural activity of a single brain and unidirectionally controls an external device (Lebedev, 2014), while the other type (sensory BMI) includes sensory feedback from the device to the brain (O’Doherty et al., 2011).”
From Human Brain/Cloud Interface (2019) · cites this paper as O’Doherty et al., 2011
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hand movements: A window into haptic object recognition
Lederman, S. J., & Klatzky, R. L. (1987) · Cognitive Psychology · doi:10.1016/0010-0285(87)90008-9 · Cited by 2,016
Brain–machine interfaces: past, present and future
Lebedev, M. A., & Nicolelis, M. A. L. (2006) · Trends in Neurosciences · doi:10.1016/j.tins.2006.07.004 · Cited by 1,875
Self-citationLearning to control a brain–machine interface for reaching and grasping by primates
Carmena, J. M., Lebedev, M. A., Crist, R. E., O'Doherty, J. E., Santucci, D. M., Dimitrov, D. F., Patil, P. G., Henriquez, C. S., & Nicolelis, M. A. L. (2003) · PLoS Biology · doi:10.1371/journal.pbio.0000042 · Cited by 1,761
Self-citationJohansson, R. S., & Westling, G. (1984) · Experimental Brain Research · doi:10.1007/bf00237997 · Cited by 1,731
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
Direct Cortical Control of 3D Neuroprosthetic Devices
Taylor, D. M., Tillery, S. I. H., & Schwartz, A. B. (2002) · Science · doi:10.1126/science.1070291 · Cited by 1,542
Real-time prediction of hand trajectory by ensembles of cortical neurons in primates
Wessberg, J., Stambaugh, C. R., Kralik, J. D., Beck, P. D., Laubach, M., Chapin, J. K., Kim, J., Biggs, S. J., Srinivasan, M. A., & Nicolelis, M. A. L. (2000) · Nature · doi:10.1038/35042582 · Cited by 1,491
Self-citationInstant neural control of a movement signal
Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R., & Donoghue, J. P. (2002) · Nature · doi:10.1038/416141a · Cited by 1,415
Maravita, A., & Iriki, A. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2003.12.008 · Cited by 1,214
Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex
Chapin, J. K., Moxon, K. A., Markowitz, R. S., & Nicolelis, M. A. L. (1999) · Nature Neuroscience · doi:10.1038/10223 · Cited by 1,173
Self-citation
Flagged first, then by citations
1 of the 31 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Enhancing Our Lives with Immersive Virtual Reality
Slater, M., & Sanchez-Vives, M. V. (2016) · Frontiers in Robotics and AI · doi:10.3389/frobt.2016.00074 · Cited by 1,993
An Integrated Brain-Machine Interface Platform With Thousands of Channels
Musk, E., & Neuralink (2019) · Journal of Medical Internet Research · doi:10.2196/16194 · Cited by 1,206
A skin-inspired organic digital mechanoreceptor
Tee, B. C.-K., Chortos, A., Berndt, A., Nguyen, A. K., Tom, A., McGuire, A., Lin, Z. C., Tien, K., Bae, W.-G., Wang, H., et al. (2015) · Science · doi:10.1126/science.aaa9306 · Cited by 882
Nature-Inspired Structural Materials for Flexible Electronic Devices
Liu, Y., He, K., Chen, G., Leow, W. R., & Chen, X. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.7b00291 · Cited by 817
Intracortical microstimulation of human somatosensory cortex
Flesher, S. N., Collinger, J. L., Foldes, S. T., Weiss, J. M., Downey, J. E., Tyler-Kabara, E. C., Bensmaia, S. J., Schwartz, A. B., Boninger, M. L., & Gaunt, R. A. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aaf8083 · Cited by 704
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation
Lebedev, M., & Nicolelis, M. A. L. (2017) · Physiological Reviews · doi:10.1152/physrev.00027.2016 · Cited by 695
Self-citationA brain-computer interface that evokes tactile sensations improves robotic arm control
Flesher, S. N., Downey, J. E., Weiss, J. M., Hughes, C. L., Herrera, A. J., Tyler-Kabara, E. C., Boninger, M. L., Collinger, J. L., & Gaunt, R. A. (2021) · Science · doi:10.1126/science.abd0380 · Cited by 617
Phillips, K. A., Bales, K. L., Capitanio, J. P., Conley, A. J., Czoty, P. W., Hart, B. A. ‘., Hopkins, W. D., Hu, S., Miller, L. A., Nader, M. A., et al. (2014) · American Journal of Primatology · doi:10.1002/ajp.22281 · Cited by 649
Pandarinath, C., Nuyujukian, P., Blabe, C. H., Sorice, B. L., Saab, J., Willett, F. R., Hochberg, L. R., Shenoy, K. V., & Henderson, J. M. (2017) · eLife · doi:10.7554/elife.18554 · Cited by 566
“…et al., 2002; Taylor et al., 2002; Carmena et al., 2003; Musallam et al., 2004; Santhanam et al., 2006; Hochberg et al., 2006; Ganguly et al., 2011; O’Doherty et al., 2011; Gilja et al., 2012; Simeral et al., 2011; Bacher et al., 2015; Nuyujukian et al., 2015; Aflalo et al., 2015; Gilja et al.,…”
BackgroundFlexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics
Heng, W., Solomon, S., & Gao, W. (2022) · Advanced Materials · doi:10.1002/adma.202107902 · Cited by 609
Donati, A. R. C., Shokur, S., Morya, E., Campos, D. S. F., Moioli, R. C., Gitti, C. M., Augusto, P. B., Tripodi, S., Pires, C. G., Pereira, G. A., et al. (2016) · Scientific Reports · doi:10.1038/srep30383 · Cited by 428
Self-citationRestoring sensorimotor function through intracortical interfaces: progress and looming challenges
Bensmaia, S. J., & Miller, L. E. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3724 · Cited by 368
Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys
Schwarz, D. A., Lebedev, M., Hanson, T., Dimitrov, D. F., Lehew, G., Meloy, J., Rajangam, S., Subramanian, V., Ifft, P. J., Li, Z., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2936 · Cited by 348
Self-citationRestoring the sense of touch with a prosthetic hand through a brain interface
Tabot, G. A., Dammann, J. F., Berg, J. A., Tenore, F. V., Boback, J. L., Vogelstein, R. J., & Bensmaia, S. J. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1221113110 · Cited by 320
Neural Stimulation and Recording with Bidirectional, Soft Carbon Nanotube Fiber Microelectrodes
Vitale, F., Santacruz, S. R., Aazhang, B., Kemere, C. T., & Pasquali, M. (2015) · ACS Nano · doi:10.1021/acsnano.5b01060 · Cited by 313
Tamayo, J. L. R., Barrio, M. G., & García, F. G. (2017) · Multimodal Technologies and Interaction · doi:10.3390/mti1040021 · Cited by 340
Feeling small: exploring the tactile perception limits
Skedung, L., Arvidsson, M., Chung, J. Y., Stafford, C. M., Berglund, B., & Rutland, M. W. (2013) · Scientific Reports · doi:10.1038/srep02617 · Cited by 285
Proprioceptive and cutaneous sensations in humans elicited by intracortical microstimulation
Armenta Salas, M., Bashford, L., Kellis, S., Jafari, M., Jo, H., Kramer, D., Shanfield, K., Pejsa, K., Lee, B., Liu, C. Y., et al. (2018) · eLife · doi:10.7554/elife.32904 · Cited by 263
Brain–machine interfaces from motor to mood
Shanechi, M. M. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0488-y · Cited by 263
Restoration of sensory information via bionic hands
Bensmaı̈a, S. J., Tyler, D. J., & Micera, S. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00630-8 · Cited by 264
Neural interfaces for the brain and spinal cord—restoring motor function
Andrew, J. O., & Zimmermann, J. B. (2012) · Nature Reviews Neurology · doi:10.1038/nrneurol.2012.219 · Cited by 260
A Brain-to-Brain Interface for Real-Time Sharing of Sensorimotor Information
Pais-Vieira, M., Lebedev, M., Kunicki, C., Wang, J., & Nicolelis, M. A. L. (2013) · Scientific Reports · doi:10.1038/srep01319 · Cited by 252
Self-citationLewis, P. M., Ackland, H. M., Lowery, A. J., & Rosenfeld, J. V. (2015) · Brain Research · doi:10.1016/j.brainres.2014.11.020 · Cited by 228
Zhou, A., Santacruz, S. R., Johnson, B. C., Alexandrov, G., Moin, A., Burghardt, F., Rabaey, J. M., Carmena, J. M., & Muller, R. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0323-x · Cited by 233
Conscious Brain-to-Brain Communication in Humans Using Non-Invasive Technologies
Grau, C., Ginhoux, R., Riera, A., Nguyen, T. L., Chauvat, H., Berg, M., Amengual, J. L., Pascual-Leone, A., & Ruffini, G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105225 · Cited by 211
Hramov, A. E., Александрович, М. В., & Pisarchik, A. N. (2021) · Physics Reports · doi:10.1016/j.physrep.2021.03.002 · Cited by 228
Assembly-specific disruption of hippocampal replay leads to selective memory deficit
Gridchyn, I., Schoenenberger, P., O’Neill, J., & Csicsvari, J. (2020) · Neuron · doi:10.1016/j.neuron.2020.01.021 · Cited by 178
He, K., Wang, C., He, Y., Su, J., & Chen, X. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00527 · Cited by 203
A review of invasive and non-invasive sensory feedback in upper limb prostheses
Svensson, P., Wijk, U., Björkman, A., & Antfolk, C. (2017) · Expert Review of Medical Devices · doi:10.1080/17434440.2017.1332989 · Cited by 192
A Self‐Powered Sensor Mimicking Slow‐ and Fast‐Adapting Cutaneous Mechanoreceptors
Chun, K., Son, Y. J., Jeon, E., Lee, S.-H., & Han, C. (2018) · Advanced Materials · doi:10.1002/adma.201706299 · Cited by 186
DARPA-funded efforts in the development of novel brain–computer interface technologies
Miranda, R. A., Casebeer, W. D., Hein, A. M., Judy, J. W., Krotkov, E., Laabs, T. L., Manzo, J. E., Pankratz, K., Pratt, G. A., Sanchez, J. C., et al. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.07.019 · Cited by 185
Cortical control of a tablet computer by people with paralysis
Nuyujukian, P., Albites Sanabria, J., Saab, J., Pandarinath, C., Jarosiewicz, B., Blabe, C. H., Franco, B., Mernoff, S. T., Eskandar, E. N., Simeral, J. D., et al. (2018) · PLOS ONE · doi:10.1371/journal.pone.0204566 · Cited by 170
Hatsopoulos, N. G., & Suminski, A. J. (2011) · Neuron · doi:10.1016/j.neuron.2011.10.020 · Cited by 179
Combining Decoder Design and Neural Adaptation in Brain-Machine Interfaces
Shenoy, K. V., & Carmena, J. M. (2014) · Neuron · doi:10.1016/j.neuron.2014.08.038 · Cited by 181
Page, D. M., George, J. A., Kluger, D. T., Duncan, C. C., Wendelken, S., Davis, T. S., Hutchinson, D. T., & Clark, G. A. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00352 · Cited by 189
Brain-Computer Interfacing: An Introduction
Rao, R. P. N. (2019) · Cited by 181
A Brain-Machine Interface Enables Bimanual Arm Movements in Monkeys
Ifft, P. J., Shokur, S., Li, Z., Lebedev, M., & Nicolelis, M. A. L. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3006159 · Cited by 187
Self-citationZhang, Y., Chen, S., Xiao, Z., Liu, X., Wu, C., Wu, K., Liu, A., Wei, D., Sun, J., Zhou, L., et al. (2021) · Advanced Healthcare Materials · doi:10.1002/adhm.202100695 · Cited by 168
A 100-Channel Hermetically Sealed Implantable Device for Chronic Wireless Neurosensing Applications
Yin, M., Borton, D. A., Aceros, J. C., Patterson, W. R., & Nurmikko, A. V. (2013) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2013.2255874 · Cited by 161
Flexible Electrodes for In Vivo and In Vitro Electrophysiological Signal Recording
Zhu, M., Wang, H., Li, S., Liang, X., Zhang, M., Dai, X., & Zhang, Y. (2021) · Advanced Healthcare Materials · doi:10.1002/adhm.202100646 · Cited by 163
Invasive vs. Non-Invasive Neuronal Signals for Brain-Machine Interfaces: Will One Prevail?
Waldert, S. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00295 · Cited by 162
McKendrick, R. D., Parasuraman, R., & Ayaz, H. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00027 · Cited by 143
Recent advances in multi-mode haptic feedback technologies towards wearable interfaces
Huang, Y., Yao, K., Li, J., Li, D., Jia, H., Liu, Y., Yiu, C. K., Park, W., & Yu, X. (2021) · Materials Today Physics · doi:10.1016/j.mtphys.2021.100602 · Cited by 148
Brain–Computer Interface after Nervous System Injury
Burns, A., Adeli, H., & Buford, J. A. (2014) · The Neuroscientist · doi:10.1177/1073858414549015 · Cited by 132
Chai, G., Sui, X., Li, S., He, L., & Lan, N. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/6/066002 · Cited by 135
Translating the Brain-Machine Interface
Thakor, N. V. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3007303 · Cited by 128
Strategies and prospects of effective neural circuits reconstruction after spinal cord injury
Yang, B., Zhang, F., Cheng, F., Ying, L., Wang, C., Shi, K., Wang, J., Xia, K., Gong, Z., Huang, X., et al. (2020) · Cell Death & Disease · doi:10.1038/s41419-020-2620-z · Cited by 128
Improved efficacy of temporally non-regular deep brain stimulation in Parkinson's disease
Brocker, D. T., Swan, B. D., Turner, D. A., Gross, R. E., Tatter, S. B., Koop, M. M., Bronte-Stewart, H. M., & Grill, W. M. (2012) · Experimental Neurology · doi:10.1016/j.expneurol.2012.09.008 · Cited by 128
Human perception of electrical stimulation on the surface of somatosensory cortex
Hiremath, S. V., Tyler-Kabara, E. C., Wheeler, J. J., Moran, D. W., Gaunt, R. A., Collinger, J. L., Foldes, S. T., Weber, D. J., Chen, W., Boninger, M. L., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0176020 · Cited by 122
Non-Invasive Brain-to-Brain Interface (BBI): Establishing Functional Links between Two Brains
Yoo, S., Kim, H., Filandrianos, E., TAGHADOS, S. J., & Park, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060410 · Cited by 132
Kim, S., Callier, T., Tabot, G. A., Gaunt, R. A., Tenore, F. V., & Bensmaia, S. J. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1509265112 · Cited by 125
Brain-Machine Interfaces beyond Neuroprosthetics
Moxon, K. A., & Foffani, G. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.036 · Cited by 126
Behavioral Demonstration of a Somatosensory Neuroprosthesis
Berg, J. A., Dammann, J. F., Tenore, F. V., Tabot, G. A., Boback, J. L., Manfredi, L. R., Peterson, M. L., Katyal, K. D., Johannes, M. S., Makhlin, A., et al. (2013) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2244616 · Cited by 119
Advances in Neuroprosthetic Learning and Control
Carmena, J. M. (2013) · PLoS Biology · doi:10.1371/journal.pbio.1001561 · Cited by 117
Shanechi, M. M., Orsborn, A. L., & Carmena, J. M. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004730 · Cited by 118
Brain-machine interfaces: an overview.
Lebedev, M. (2014) · Translational Neuroscience · doi:10.2478/s13380-014-0212-z · Cited by 112
Self-citationPerceiving invisible light through a somatosensory cortical prosthesis
Thomson, E. E., Carra, R. B., & Nicolelis, M. A. L. (2013) · Nature Communications · doi:10.1038/ncomms2497 · Cited by 117
Self-citationJohnson, L., Wander, J. D., Sarma, D., Su, D. K., Fetz, E. E., & Ojemann, J. G. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/3/036021 · Cited by 111
A cognitive neuroprosthetic that uses cortical stimulation for somatosensory feedback
Klaes, C., Shi, Y., Kellis, S., Minxha, J., Revechkis, B., & Andersen, R. A. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/5/056024 · Cited by 110
Richardson, M. D., Parker, J. J., & Waziri, A. E. (2012) · Neurology Clinical Practice · doi:10.1212/cpj.0b013e31826af22b · Cited by 111
Brain Computer Interfaces in Rehabilitation Medicine
Bockbrader, M. A., Francisco, G. E., Lee, R., Olson, J. D., Solinsky, R. J., & Boninger, M. L. (2018) · PM&R · doi:10.1016/j.pmrj.2018.05.028 · Cited by 116
Memristive synapses connect brain and silicon spiking neurons
Serb, A., Corna, A., George, R., Khiat, A., Rocchi, F., Reato, M., Maschietto, M., Mayr, C., Indiveri, G., Vassanelli, S., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-58831-9 · Cited by 102
Neuroprosthetics: from sensorimotor to cognitive disorders
Gupta, A., Vardalakis, N., & Wagner, F. B. (2023) · Communications Biology · doi:10.1038/s42003-022-04390-w · Cited by 113
Interfacing the Somatosensory System to Restore Touch and Proprioception: Essential Considerations
Weber, D. J., Friesen, R., & Miller, L. E. (2012) · Journal of Motor Behavior · doi:10.1080/00222895.2012.735283 · Cited by 98
Ownership of an artificial limb induced by electrical brain stimulation
Collins, K. L., Guterstam, A., Cronin, J. A., Olson, J. D., Ehrsson, H. H., & Ojemann, J. G. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1616305114 · Cited by 98
From thought to action: The brain–machine interface in posterior parietal cortex
Andersen, R. A., Aflalo, T., & Kellis, S. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902276116 · Cited by 94
Security in Brain-Computer Interfaces
Bernal, S. L., Celdrán, A. H., Pérez, G. M., Barros, M. T., & Balasubramaniam, S. (2021) · ACM Computing Surveys · doi:10.1145/3427376 · Cited by 110
In vivo assessment of human brain oscillations during application of transcranial electric currents
Soekadar, S. R., Witkowski, M., Cossio, E. G., Birbaumer, N., Robinson, S. E., & Cohen, L. G. (2013) · Nature Communications · doi:10.1038/ncomms3032 · Cited by 96
Triboelectric nanogenerators enabled sensing and actuation for robotics
Liu, S., Li, Y., Guo, W., Huang, X., Xu, L., Lai, Y., Zhang, C., & Wu, H. (2019) · Nano Energy · doi:10.1016/j.nanoen.2019.104005 · Cited by 95
Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review.
Ghafoor, U., Kim, S., & Hong, K.-S. (2017) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2017.00059 · Cited by 88
Intracortical Recording Interfaces: Current Challenges to Chronic Recording Function
Gunasekera, B., Saxena, T. K., Bellamkonda, R. V., & Karumbaiah, L. (2015) · ACS Chemical Neuroscience · doi:10.1021/cn5002864 · Cited by 96
Humanlike robot hands controlled by brain activity arouse illusion of ownership in operators
Alimardani, M., Nishio, S., & Ishiguro, H. (2013) · Scientific Reports · doi:10.1038/srep02396 · Cited by 93
Rapid Integration of Artificial Sensory Feedback during Operant Conditioning of Motor Cortex Neurons
Prsa, M., Galiñanes, G. L., & Huber, D. (2017) · Neuron · doi:10.1016/j.neuron.2017.01.023 · Cited by 97
Martins, N. R. B., Angelica, A., Chakravarthy, K., Svidinenko, Y., Boehm, F. J., Opris, I., Lebedev, M. A., Swan, M., Garan, S. A., Rosenfeld, J. V., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00112 · Cited by 91
Self-citationMaksimenko, V., Павлов, А. Н., Евгеньевна, Р. А., Nedaivozov, V. O., Grubov, V., Koronovslii, A., Pchelintseva, S., Николаевна, П. Е., Pisarchik, A. N., & Hramov, A. E. (2018) · Nonlinear Dynamics · doi:10.1007/s11071-018-4047-y · Cited by 95
Engineering Artificial Somatosensation Through Cortical Stimulation in Humans
Lee, B., Kramer, D. R., Salas, M. A., Kellis, S., Brown, D., Dobreva, T., Klaes, C., Heck, C., Liu, C. Y., & Andersen, R. A. (2018) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2018.00024 · Cited by 94
Fifer, M. S., McMullen, D. P., Osborn, L. E., Thomas, T. M., Christie, B., Nickl, R. W., Candrea, D. N., Pohlmeyer, E. A., Thompson, M. C., Anaya, M. A., et al. (2022) · Neurology · doi:10.1212/wnl.0000000000013173 · Cited by 103
Neuroprosthetic technology for individuals with spinal cord injury
Collinger, J. L., Foldes, S. T., Bruns, T. M., Wodlinger, B., Gaunt, R. A., & Weber, D. J. (2013) · Journal of Spinal Cord Medicine · doi:10.1179/2045772313y.0000000128 · Cited by 91
Information Systems Opportunities in Brain–Machine Interface Decoders
Kao, J. C., Stavisky, S. D., Sussillo, D., Nuyujukian, P., & Shenoy, K. V. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2307357 · Cited by 85
Expanding the primate body schema in sensorimotor cortex by virtual touches of an avatar
Shokur, S., O’Doherty, J. E., Winans, J., Bleuler, H., Lebedev, M., & Nicolelis, M. A. L. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1308459110 · Cited by 84
Self-citationTemporary-tattoo for long-term high fidelity biopotential recordings
Bareket, L., Inzelberg, L., Rand, D., David‐Pur, M., Rabinovich, D., Brandes, B., & Hanein, Y. (2016) · Scientific Reports · doi:10.1038/srep25727 · Cited by 88
STIMULUS: Noninvasive Dynamic Patterns of Neurostimulation Using Spatio-Temporal Interference
Cao, J., & Grover, P. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2919912 · Cited by 85
Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates
Rajangam, S., Tseng, P.-H., Yin, A. S., Lehew, G., Schwarz, D., Lebedev, M., & Nicolelis, M. A. L. (2016) · Scientific Reports · doi:10.1038/srep22170 · Cited by 84
Self-citationRestoring tactile and proprioceptive sensation through a brain interface
Tabot, G. A., Kim, S. S., Winberry, J. E., & Bensmaia, S. J. (2015) · Neurobiology of Disease · doi:10.1016/j.nbd.2014.08.029 · Cited by 83
Gerhard, F., Deger, M., & Truccolo, W. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005390 · Cited by 83
Shanechi, M. M., Williams, Z. M., Wornell, G. W., Hu, R. C., Powers, M. E., & Brown, E. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059049 · Cited by 85
Brain-Machine Interfaces: Powerful Tools for Clinical Treatment and Neuroscientific Investigations
Slutzky, M. W. (2018) · The Neuroscientist · doi:10.1177/1073858418775355 · Cited by 86
The Evolution of Neuroprosthetic Interfaces
Adewole, D. O., Serruya, M. D., Harris, J. P., Burrell, J. C., Petrov, D., Chen, H.-C. I., Wolf, J. A., & Cullen, D. K. (2016) · Critical Reviews in Biomedical Engineering · doi:10.1615/critrevbiomedeng.2016017198 · Cited by 86
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
Modulating Brain Activity with Invasive Brain–Computer Interface: A Narrative Review
Zhao, Z., Nie, C., Jiang, C.-T., Cao, S.-H., Tian, K.-X., Yu, S., & Gu, J. (2023) · Brain Sciences · doi:10.3390/brainsci13010134 · Cited by 87
Neurobionics and the brain–computer interface: current applications and future horizons
Rosenfeld, J. V., & Wong, Y. T. (2017) · The Medical Journal of Australia · doi:10.5694/mja16.01011 · Cited by 81
Design and validation of a real-time spiking-neural-network decoder for brain–machine interfaces
Dethier, J., Nuyujukian, P., Ryu, S. I., Shenoy, K. V., & Boahen, K. A. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/3/036008 · Cited by 76
Fifer, M. S., Acharya, S., Benz, H. L., Mollazadeh, M., Crone, N. E., & Thakor, N. V. (2012) · IEEE Pulse · doi:10.1109/mpul.2011.2175636 · Cited by 74
The effects of chronic intracortical microstimulation on neural tissue and fine motor behavior
Rajan, A. T., Boback, J. L., Dammann, J. F., Tenore, F. V., Wester, B. A., Otto, K. J., Gaunt, R. A., & Bensmaia, S. J. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/6/066018 · Cited by 76
Shokur, S., Gallo, S., Moioli, R. C., Donati, A. R. C., Morya, E., Bleuler, H., & Nicolelis, M. A. L. (2016) · Scientific Reports · doi:10.1038/srep32293 · Cited by 76
Self-citationBioinspired Prosthetic Interfaces
Li, P., Ali, H. P. A., Cheng, W. M., Yang, J., & Tee, B. C. K. (2020) · Advanced Materials Technologies · doi:10.1002/admt.201900856 · Cited by 76
Single trial prediction of self-paced reaching directions from EEG signals
Lew, E. Y. L., Chavarriaga, R., Silvoni, S., & Millán, J. D. R. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00222 · Cited by 76
Virtual Active Touch Using Randomly Patterned Intracortical Microstimulation
O’Doherty, J. E., Lebedev, M., Li, Z., & Nicolelis, M. A. L. (2011) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2011.2166807 · Cited by 73
Self-citationSilva, G. A. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00843 · Cited by 74
Biological and bionic hands: natural neural coding and artificial perception
Bensmaia, S. J. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0209 · Cited by 67
Metadata
- Authors
- O’Doherty, J. E., Lebedev, M. A., Ifft, P. J., Zhuang, K. Z., Shokur, S., Bleuler, H., & Nicolelis, M. A. L.
- Affiliations
- Duke University (US)
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Nicolelis, Miguel A. L.
- Venue
- Nature
- Published
- 2011-10-05
- Volume
- 479
- Issue
- 7372
- Pages
- 228-231
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3236080
- OpenAlex
- W2037567991
- Cited by
- 629
- References
- 31
- Citations by year
- 2026: 25 · 2025: 23 · 2024: 30 · 2023: 34 · 2022: 35 · 2021: 40 · 2020: 36 · 2019: 43 · 2018: 42 · 2017: 41 · 2016: 51 · 2015: 60 · 2014: 76 · 2013: 61 · 2012: 27
- FWCI
- 27.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion)
- Funders
- National Institutes of Health (DP1OD006798)
- National Institute of Mental Health (DP1MH099903)
- National Institute of Neurological Disorders and Stroke (R01NS073125, NS073125)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (RC1HD063390)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- intracortical microstimulation, somatosensory cortex, primary motor cortex, neural prostheses, reaching movements
- MeSH terms
- Algorithms, Animals, Artificial Limbs, Brain, Feedback, Macaca mulatta, Man-Machine Systems, Psychometrics, Reward, Somatosensory Cortex, Touch, User-Computer Interface
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
