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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).”
From Human Brain/Cloud Interface (2019) · cites this paper as 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
Self-citation
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Leong, F., Micera, S., & Shokur, S. (2025) · APL Bioengineering · doi:10.1063/5.0249434
Self-citationGerrity, C. G., Treuting, R. L., Peters, R. A., & Womelsdorf, T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.20.671126
Peresada, D., Mzeihem, M., & Rybalko, D. A. (2025) · Springer eBooks · doi:10.1007/978-3-032-04688-8_26
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Chan, R. H. M., Mak, T., & Tin, C. (2014) · Springer eBooks · doi:10.1007/978-1-4614-8151-5_12
Tolboom, A. T., Hucher, G., Shulz, D. E., & Ego‐Stengel, V. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.08.698338
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Consulting Patent Associate, G. A. L., & Ravi, S. (2026) · INDIAN DRUGS · doi:10.53879/id.63.02.p0005
Chaubey, M. K., Jasrotia, A., Jain, P., & Quraishi, S. J. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-19242-4_19
A new BCI paradigm based on biological brain – digital twin brain dialogue
Zhang, T., Zhang, R., Zeng, X., Zeng, M., Xu, Y., Xiong, Y., Zhang, G., Guo, D., & Yao, D. (2026) · Cognitive Neurodynamics · doi:10.1007/s11571-026-10439-x · Cited by 2
Albrecht, S., Vandevelde, J. R., Vecchi, E., Berra, G., Bassetti, D., Stüttgen, M. C., Luhmann, H. J., & Horenko, I. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1605209
Sensorimotor integration in the moving spinal cord
Knafo, S. (2015) · theses.fr (ABES) · doi:10.70675/80d97ac7z5404z4577za1d5z1c8381c5051c
Khoyratee, F. (2019) · theses.fr (ABES) · doi:10.70675/a933e7d1z8dbbz411fzbdf3z11ada3ab3c35
Abbasi, A. (2019) · theses.fr (ABES) · doi:10.70675/d2187cf3z47a0z4420z8005zb0c6f6b4e73f
Optimizing cortical feedback strategies for a closed-loop brain machine interface
Lassagne, H. (2023) · theses.fr (ABES) · doi:10.70675/779d5bbaz5470z4faczb771z288a715634b1
Mangalam, M., & Deligiannis, T. (2026) · Ecological Psychology · doi:10.1080/10407413.2026.2651681
Ye, A., Wang, X., Song, Y., Li, Y., Yu, S., & Zhang, D. (2026) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2026.3699212
Materials and technologies in neural interfaces: optimization ways for chronic implantation
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Flexible E-textile electrodes for human-machine interfaces
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Zhao, X., Chen, K., Ma, J., Wang, B., Xu, C., Hu, B., & Chen, H. (2026) · Spine Research · doi:10.1097/br9.0000000000000041
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Yang, R., Xu, S., Pang, W., & Zhang, Y. (2026) · Advanced Materials · doi:10.1002/adma.74589
Su, E., Retheesh, V., Kumaravelu, K., Sharma, S., Grill, W. M., & Otto, K. (2026) · Scientific Reports · doi:10.21203/rs.3.rs-10517334/v1
Peixoto, T. G. (2026) · doi:10.67665/crivum.yf37vpzc
Zheng, K., Yu, W., Gu, Y., He, Z., Wang, X., & Li, Y. (2026) · ACS Materials Letters · doi:10.1021/acsmaterialslett.6c00685
Yan-long, L. I., Ying, W. A. N. G., Long, S., Wan-yu, S. U. N., Yue, L. I., Wen-xin, J. I. A. O., Jia-ning, W. A. N. G., & Ji-lu, L. I. N. (2026) · 世界针灸杂志：英文版 · doi:10.1016/j.wjam.2026.09.006
Pang, B., Yang, G., Cheng, Q., Zhou, S., Wang, Z., Wang, X., Qiu, Y., Gong, H., Bu, T., Xie, B., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01814-4
Materials and Technologies in Neural Interfaces: Pathways to Optimization for Chronic Implantation
Родионова, К. Н., Vigovskaya, E. A., Novosad, Y. A., Shabunin, A. S., & Vissarionov, S. V. (2026) · Neuroscience and Behavioral Physiology · doi:10.1007/s11055-026-02078-w
Recurrent Brain-Computer Interfaces
Andrew, J. O. (2014) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_706-1
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
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