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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.
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“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]
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“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).”
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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
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Kim, S., Hyun, S.-Y., Sohn, J.-W., Chae, S., & Kim, S.-P. (2019) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2019.8737255 · Cited by 2
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Bensmaı̈a, S. J. (2015) · IEEE World Haptics Conference (WHC) · doi:10.1109/whc.2015.7177674 · Cited by 2
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Keestra, M. (2014) · UvA-DARE (University of Amsterdam) · Cited by 2
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Berkowitz, A. (2012) · Encyclopedia of Life Sciences · doi:10.1002/9780470015902.a0000189.pub3 · Cited by 2
Song, W., Kerr, C. C., Lytton, W. W., & Francis, J. T. (2013) · PLoS ONE · doi:10.1371/annotation/a697686c-a062-4520-a9df-d5d0bbb42c7d · Cited by 2
Guo, T., Chang, Y.-C., Li, L., Dokos, S., & Li, L. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1116900 · Cited by 2
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Ryun, S., & Chung, C. K. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.18.574786 · Cited by 2
Xie, C., Wang, L., Yang, J., & Guo, J. (2025) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2025.110483 · Cited by 1
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Cursi, M., & Filippi, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-86878-8_21 · Cited by 2
Zhang, Q., Liu, B., Wang, Z., Zhou, J., Yang, X., Zhou, Q., Zhao, Y., Li, S., Zhou, J., & Wang, C. (2025) · Advanced Science · doi:10.1002/advs.202503011 · Cited by 2
Sehnan, M., Li, H., Li, X., Grebogi, C., Gao, Z., & Dang, W. (2026) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2026.110704 · Cited by 1
Kozai, T. D. Y., Hooks, B. M., Vazquez, A. L., Gharbawie, O. A., Huang, C., Graczyk, E. L., Smith, M. A., Taylor, D. M., Hernandez-Reynoso, A. G., London, A. J., et al. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae54cf · Cited by 2
Cheng, Y., 郭向奎, Dong, L., Deng, Q., Qiu, M., & Luo, Z. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1861673 · Cited by 1
Millán, J. D. R. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-398358-9.00063-x · Cited by 1
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Dadarlat, M. C., O’Doherty, J. E., & Sabes, P. N. (2014) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-319-09979-8_4 · Cited by 1
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Sengül, A., Shokur, S., & Bleuler, H. (2014) · Mechanisms and machine science · doi:10.1007/978-3-319-05431-5_17 · Cited by 1
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Shurkhay, V. A., Александрова, Е. В., Aa, P., & Goryaynov, S. A. (2015) · Burdenko s Journal of Neurosurgery · doi:10.17116/neiro201579197-104 · Cited by 1
The Brain-Machine Interface (BMI) : A Novel Neurotechnology
Yoshimine, T., Hirata, M., Yanagisawa, T., & Kishima, H. (2016) · Japanese Journal of Neurosurgery · doi:10.7887/jcns.25.964 · Cited by 1
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Tepe, V., Salas-Snyder, S., & Peterson, C. M. (2017) · Springer eBooks · doi:10.1007/978-1-4939-7247-0_1 · Cited by 1
Hramov, A. E., Frolov, N. S., & Musatov, V. Y. (2018) · Dynamics and Fluctuations in Biomedical Photonics · doi:10.1117/12.2291675 · Cited by 1
Kurkin, S., Grubov, V., Musatov, V. Y., Евгеньевна, Р. А., & Pchelintseva, S. (2018) · Dynamics and Fluctuations in Biomedical Photonics · doi:10.1117/12.2291688 · Cited by 1
Augmentation Through Interconnection: Brain-Nets and Telemedicine
Lebedev, M., Shaderkin, I. A., Ryabkov, I. V., & Lebedev, G. S. (2021) · Contemporary clinical neuroscience · doi:10.1007/978-3-030-54564-2_16 · Cited by 1
Self-citationFrancis, J. T., Rozenboym, A. V., von Kraus, L. M., Xu, S., Chhatbar, P. Y., Semework, M., Hawley, E. S., & Chapin, J. K. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.812837 · Cited by 1
Design and performance study of a BMI-based hand-assisted robot
Cai, J., & Cai, J. (2022) · IEEE International Conference on Data Science and Computer Application (ICDSCA) · doi:10.1109/icdsca56264.2022.9987939 · Cited by 1
Artificial Sensory Feedback to the Brain: Somatosensory Feedback for Neural Devices and BCI
Bjånes, D. A., & Moritz, C. T. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_111 · Cited by 1
A Challenge for Bringing a BCI Closer to Motor Control: The “Interface Uncanny Valley” Hypothesis
Yashin, A. S. (2023) · IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB) · doi:10.1109/csgb60362.2023.10329830 · Cited by 1
Tomonaga, M., Tanaka, Y., & Sakai, M. (2024) · Primates · doi:10.1007/s10329-023-01117-1 · Cited by 2
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Kim, H. (2023) · MechEcology · doi:10.23104/me.2023.10.2.2.12 · Cited by 1
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Friesen, L. (2013) · doi:10.7275/5389-r721 · Cited by 1
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Li, H., & Zhou, L. (2020) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.0c00093 · Cited by 1
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Shi, Z. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85380-4.00013-0 · Cited by 1
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Zanos, S. P. (2013) · ResearchWorks at the University of Washington (University of Washington) · Cited by 1
Song, W., Kerr, C. C., Lytton, W. W., & Francis, J. T. (2013) · PLoS ONE · doi:10.1371/annotation/26c3d7a9-80dc-4719-bf61-968e5f322983 · Cited by 1
Opriș, I. C., & Ferrera, V. P. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.12.007 · Cited by 1
RetractedNeurophysiological correlates of tactile width discrimination in humansPreprint
Pais-Vieira, M., Pais-Vieira, C., Allahdad, M. K., Perrotta, A., André, P., Kunicki, C. B., & Manuel, O. (2022) · Research Square · doi:10.21203/rs.3.rs-1328335/v1 · Cited by 1
Human Single Neuron RecordingsPreprint
Fu, Z., & Rutishauser, U. (2024) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/57wzm · Cited by 1
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Orsborn, A. L., & Carmena, J. M. (2018) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_505-3
Mahato, N. K. (2015) · Journal of Bangladesh Society of Physiologist · doi:10.3329/jbsp.v9i1.22795
Movement prediction with sensory feedback and environmental interaction
Chung, C. K., Yeom, H. G., Lee, H., Ryun, S., & Kim, J. S. (2014) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2014.6782546
Monkey brains 'feel' virtual objects
Young, S. (2011) · Nature · doi:10.1038/news.2011.576
Human Transgenesis: Definitions, Technical Possibilities and Moral Challenges
da Fonseca, F. G., Ribeiro, D. M., Carvalho, N. P., Lara, M. A., Marçal, A. C., & Stancioli, B. (2012) · Philosophy & Technology · doi:10.1007/s13347-012-0074-7
Balasubramanian, K., & Hatsopoulos, N. G. (2015) · Springer eBooks · doi:10.1007/978-1-4614-6675-8_705
Investigation of Methods for Assessing Sensorimotor Performance in Humans and Monkeys
Quick, K. M. (2015) · D-Scholarship@Pitt (University of Pittsburgh)
implications for brain-machine interface control Motor cortical control of movement speed with
Yu, B. M., Schwartz, A. B., & Chase, S. M. (2014)
Hokanson, J. (2014) · D-Scholarship@Pitt (University of Pittsburgh)
Conclusion: Personal electronics and distributed theranostics
Carrara, S. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781139629539.052
Motor output-type Brain-Machine Interface
Suzuki, T. (2012) · The Brain & Neural Networks · doi:10.3902/jnns.19.112
Intercontinental mind-meld unites two rats
Yong, E. (2013) · Nature · doi:10.1038/nature.2013.12522
Wireless communication links for brain-machine interface applications
Larson, L. E. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2224845
Designing Closed-Loop Brain-Machine Interfaces Using Model Predictive Control
Kumar, G., Kothare, M. V., Thakor, N. V., Schieber, M. H., Pan, H., Ding, B., & Zhong, W. (2016) · Technologies · doi:10.3390/technologies4020018
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Franco, M., Ortiz, T. V., de Amorim, H. A., & Faber, J. (2016) · Advances in media, entertainment and the arts (AMEA) book series · doi:10.4018/978-1-5225-0510-5.ch011
Long-term optical stimulation of channelrhodopsin-expressing neurons to study network
Lignani, G., Ferrea, E., Difato, F., Amarù, J., Ferroni, E., Lugarà, E., Espinoza, S., Gainetdinov, R. R., Baldelli, P., & Benfenati, F. (2013)
Quality improvement in home life based on EEG signal
Wang, X., Wu, S., Wang, S., & Liang, J. (2017) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/69/1/012103
Neural networks-on-chip for hybrid bio-electronic systems
Coapes, G. (2016)
Collaborative Model in Brain-Computer Integration
Shi, Z., Ma, G., & Li, J. (2017) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-68121-4_2
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Suzuki, T. (2017) · Journal of the Japan Society for Precision Engineering · doi:10.2493/jjspe.83.996
Commentary: Emergence of a Stable Cortical Map for Neuroprosthetic Control
Lebedev, M. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00642
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Wang, L., Xue, X., & Zhan, Y. (2018) · IEEE International Conference on Cyborg and Bionic Systems (CBS) · doi:10.1109/cbs.2018.8612278
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Chen, W., Zhang, S., Hao, Y., Xu, K., Zheng, N., & Zheng, X. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-814942-3.00001-5
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Rodenkirch, C. (2020) · doi:10.7916/d8-g6pv-0k44
Brain Automation, Panacea for Physical Disabilities
Rathna, R. (2020) · International Conference on Computational Performance Evaluation (ComPE) · doi:10.1109/compe49325.2020.9200106
Marasco, P. D. (2013) · Series in medical physics and biomedical engineering · doi:10.1201/b15079-13
Role of Brain Cortex Plasticity in the Use of Neural Interfaces
Badakva, A. M., Miller, N. V., Zobova, L. N., & Roshchin, V. Y. (2020) · Human Physiology · doi:10.1134/s0362119720070026
Continuity within somatosensory cortical map shapes the integration of optogenetic inputPreprint
Lassagne, H., Goueytes, D., Shulz, D. E., Estebanez, L., & Ego‐Stengel, V. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.26.437211
Being Machine: Two Competing Models for Neuroprosthesis
Shores, C. (2015) · Philosophy and medicine · doi:10.1007/978-94-017-9870-9_16
Neuro-fuzzy control system of a mobile plant using the brain computer interface
Stepanov, M. F., Stepanov, A. M., & Stepanov, O. I. (2021) · International Conference on Information Technology and Nanotechnology (ITNT) · doi:10.1109/itnt52450.2021.9649298
Carter, M. E., Essner, R. A., Goldstein, N., & Iyer, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-818646-6.00010-5
Can We Induce a Cognitive Representation of a Prosthetic Arm by Means of Crossmodal Stimuli?
Franco, M., Ortiz, T. V., de Amorim, H. A., & Faber, J. (2019) · IGI Global eBooks · doi:10.4018/978-1-7998-1754-3.ch079
Millán, J. D. R., & Perdikis, S. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818422-6.00059-9
Bensmaı̈a, S. J. (2013) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_561-1
Functional Neuroscience: Cortical Control of Limb Prosthesis
Orsborn, A. L., & Carmena, J. M. (2013) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_505-2
Functional Neuroscience: Cortical Control of Limb Prostheses
Orsborn, A. L., & Carmena, J. M. (2022) · Springer eBooks · doi:10.1007/978-1-0716-1006-0_505
Balasubramanian, K., & Hatsopoulos, N. G. (2022) · Springer eBooks · doi:10.1007/978-1-0716-1006-0_705
Bensmaı̈a, S. J. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_561
Recurrent Brain-Computer Interfaces
Jackson, A. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_706
Recurrent Brain-Computer Interfaces
Andrew, J. O. (2015) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-6675-8_706
Balasubramanian, K., & Hatsopoulos, N. G. (2014) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_705-1
Luo, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.27.522047
Ifft, P. J. (2014) · DukeSpace (Duke University)
Self-citationA Framework for Brain-Computer Interfaces Closed-Loop Communication Systems
Cu, M., Peko, G. M., Chan, J., & Sundaram, D. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-28816-6_6
Brain Co-processors: Ethical and Social Implications
Rao, R. P. N., & Schönau, A. (2023) · Advances in neuroethics · doi:10.1007/978-3-031-26801-4_10 · Cited by 1
Kudela, P., Fifer, M. S., Osborn, L. E., McMullen, D. P., Celnik, P. A., Cantarero, G. L., Tenore, F. V., & Anderson, W. S. (2023) · medRxiv · doi:10.1101/2023.06.22.23291761
Tomonaga, M., & Tanaka, Y. (2023) · doi:10.31234/osf.io/a3hj9
Designing Touch: Intracortical Neurohaptic Feedback in Virtual Reality
Paschall, C. J., Hauptman, J. S., Rao, R. P. N., Ojemann, J. G., & Herron, J. A. (2023) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-031-49457-4_10
Fronto-Central Changes in Multiple Frequency Bands in Active Tactile Width Discrimination Task
Ramos, T., Ramos, J., Pais-Vieira, C., & Pais-Vieira, M. (2024) · Brain Science · doi:10.3390/brainsci14090915
Khani, J., Nayar, G., Thomson, E. E., & Nicolelis, M. A. L. (2024) · Brain Network Disorders · doi:10.1016/j.bnd.2024.11.001
Self-citationChen, L., Zhang, Y., Mo, L., Guo, Z., Gao, F., Liang, F., Liao, W., & Yin, M. (2024) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio64047.2024.10907704
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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 ›
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