Active tactile exploration using a brain–machine–brain interface

Hand movements: A window into haptic object recognition

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Brain–machine interfaces: past, present and future

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Learning to control a brain–machine interface for reaching and grasping by primates

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Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects

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Cortical control of a prosthetic arm for self-feeding

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Direct Cortical Control of 3D Neuroprosthetic Devices

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Real-time prediction of hand trajectory by ensembles of cortical neurons in primates

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Instant neural control of a movement signal

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Tools for the body (schema)

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Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex

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A closed-loop brain–machine interface framework design for motor rehabilitation

Pan, H., Mi, W., Lei, X., & Deng, J. P. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101877 · Cited by 32

A training platform for many-dimensional prosthetic devices using a virtual reality environment

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Dynamic Poly(3,4‐ethylenedioxythiophene)s Integrate Low Impedance with Redox‐Switchable Biofunction

Lin, H., Zhu, B., Wu, Y., Sekine, J., Nakao, A., Luo, S., Yamashita, Y., & Yu, H. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201703890 · Cited by 32

Reward signals in the motor cortex: from biology to neurotechnology

Derosière, G., Shokur, S., & Vassiliadis, P. (2025) · Nature Communications · doi:10.1038/s41467-024-55016-0 · Cited by 30

Electrochemical layer-by-layer approach to fabricate mechanically stable platinum black microelectrodes using a mussel-inspired polydopamine adhesive

Kim, R., & Nam, Y. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/2/026010 · Cited by 32

An Ultraflexible Electrode Array for Large‐Scale Chronic Recording in the Nonhuman Primate Brain

Tian, Y., Yin, J., Wang, C., He, Z., Xie, J., Feng, X., Zhou, Y., Ma, T., Xie, Y., Li, X., et al. (2023) · Advanced Science · doi:10.1002/advs.202302333 · Cited by 32

A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks

Keren, H., Partzsch, J., Marom, S., & Mayr, C. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00432 · Cited by 30

Degraded EEG decoding of wrist movements in absence of kinaesthetic feedback

Galán, F., Baker, M. R., Alter, K., & Baker, S. N. (2014) · Human Brain Mapping · doi:10.1002/hbm.22653 · Cited by 30

Neuroplasticity subserving the operation of brain–machine interfaces

Oweiss, K. G., & Badreldin, I. S. (2015) · Neurobiology of Disease · doi:10.1016/j.nbd.2015.05.001 · Cited by 33

New Perspectives on Neuroengineering and Neurotechnologies: NSF-DFG Workshop Report

Moritz, C. T., Ruther, P., Goering, S., Stett, A., Ball, T., Burgard, W., Chudler, E. H., & Rao, R. P. N. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2543662 · Cited by 30

Biomechanics and neural control of movement, 20 years later: what have we learned and what has changed?

Nordin, A. D., Rymer, W. Z., Biewener, A. A., Schwartz, A. B., Chen, D., & Horak, F. B. (2017) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-017-0298-y · Cited by 32

Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain–Computer Interfaces

Even-Chen, N., Stavisky, S. D., Pandarinath, C., Nuyujukian, P., Blabe, C. H., Hochberg, L. R., Henderson, J. M., & Shenoy, K. V. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2776204 · Cited by 31

Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior

Loriette, C., Amengual, J. L., & Hamed, S. B. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.811736 · Cited by 32

Automatic navigation for rat-robots with modeling of the human guidance

Sun, C., Zheng, N., Zhang, X., Chen, W., & Zheng, X. (2013) · Journal of Bionic Engineering · doi:10.1016/s1672-6529(13)60198-5 · Cited by 29

A fast intracortical brain–machine interface with patterned optogenetic feedback

Abbasi, A., Goueytes, D., Shulz, D. E., Ego‐Stengel, V., & Estebanez, L. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aabb80 · Cited by 29

Neural mechanisms of the temporal response of cortical neurons to intracortical microstimulation

Kumaravelu, K., & Grill, W. M. (2024) · Brain stimulation · doi:10.1016/j.brs.2024.03.012 · Cited by 27

Adaptive Inverse Control of Neural Spatiotemporal Spike Patterns With a Reproducing Kernel Hilbert Space (RKHS) Framework

Li, L., Park, I. M., Brockmeier, A. J., Chen, B., Seth, S., Francis, J. T., Sanchez, J. C., & Príncipe, J. C. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2200300 · Cited by 28

Cyberattacks on Miniature Brain Implants to Disrupt Spontaneous Neural Signaling

Bernal, S. L., Celdrán, A. H., Maimó, L. F., Barros, M. T., Balasubramaniam, S., & Pérez, G. M. (2020) · IEEE Access · doi:10.1109/access.2020.3017394 · Cited by 28

Towards a Naturalistic Brain-Machine Interface: Hybrid Torque and Position Control Allows Generalization to Novel Dynamics

Chhatbar, P. Y., & Francis, J. T. (2013) · PLoS ONE · doi:10.1371/journal.pone.0052286 · Cited by 28

Interbrain cortical synchronization encodes multiple aspects of social interactions in monkey pairs

Tseng, P.-H., Rajangam, S., Lehew, G., Lebedev, M., & Nicolelis, M. A. L. (2018) · Scientific Reports · doi:10.1038/s41598-018-22679-x · Cited by 28

Decoding the individual finger movements from single‐trial functional magnetic resonance imaging recordings of human brain activity

Shen, G., Zhang, J., Wang, M., Lei, D., Yang, G., Zhang, S., & Du, X. (2014) · European Journal of Neuroscience · doi:10.1111/ejn.12547 · Cited by 27

A neuro-inspired model-based closed-loop neuroprosthesis for the substitution of a cerebellar learning function in anesthetized rats

Hogri, R., Bamford, S. A., Taub, A. H., Magal, A., Giudice, P. D., & Mintz, M. (2015) · Scientific Reports · doi:10.1038/srep08451 · Cited by 26

Intracortical Brain-Machine Interfaces Advance Sensorimotor Neuroscience

Schroeder, K. E., & Chestek, C. A. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00291 · Cited by 28

The PennBMBI: A general purpose wireless Brain-Machine-Brain Interface system for unrestrained animals

Liu, X., Subei, B., Zhang, M., Richardson, A. G., Lucas, T. H., & der Spiegel, J. V. (2014) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2014.6865219 · Cited by 24

Clinical neuroprosthetics: Today and tomorrow

Lee, M. B., Kramer, D. R., Peng, T. D., Barbaro, M. F., Liu, C. Y., Kellis, S., & Lee, B. (2019) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2019.07.056 · Cited by 29

Adversarial robustness benchmark for EEG-based brain–computer interfaces

Meng, L., Jiang, X., & Wu, D. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.01.028 · Cited by 29

Hand-in-hand advances in biomedical engineering and sensorimotor restoration

Pisotta, I., Perruchoud, D., & Ionta, S. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.03.003 · Cited by 27

Shaping the Dynamics of a Bidirectional Neural Interface

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Short reaction times in response to multi-electrode intracortical microstimulation may provide a basis for rapid movement-related feedback

Sombeck, J. T., & Miller, L. E. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab5cf3 · Cited by 24

Bacomics: a comprehensive cross area originating in the studies of various brain–apparatus conversations

Yao, D., Zhang, Y., Liu, T., Xu, P., Gong, D., Lu, J., Xia, Y., Luo, C., Guo, D., Dong, L., et al. (2020) · Cognitive Neurodynamics · doi:10.1007/s11571-020-09577-7 · Cited by 25

User Identity Protection in EEG-Based Brain–Computer Interfaces

Meng, L., Jiang, X., Huang, J., Li, W., Luo, H., & Wu, D. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3310883 · Cited by 32

Erratum to: Implantable neurotechnologies: bidirectional neural interfaces—applications and VLSI circuit implementations

Greenwald, E., Masters, M. R., & Thakor, N. V. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-016-1452-6 · Cited by 23

Optimal space–time precoding of artificial sensory feedback through mutichannel microstimulation in bi-directional brain–machine interfaces

Daly, J., Liu, J., Aghagolzadeh, M., & Oweiss, K. G. (2012) · Journal of Neural Engineering · doi:10.1088/1741-2560/9/6/065004 · Cited by 23

Technical considerations for generating somatosensation via cortical stimulation in a closed-loop sensory/motor brain-computer interface system in humans

Kramer, D. R., Kellis, S., Barbaro, M. F., Salas, M. A., Nune, G. C., Liu, C. Y., Andersen, R. A., & Lee, B. (2019) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2019.01.027 · Cited by 23

Restoring Somatosensation: Advantages and Current Limitations of Targeting the Brainstem Dorsal Column Nuclei Complex

Loutit, A. J., & Potas, J. R. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00156 · Cited by 22

Rapid and transient enhancement of thalamic information transmission induced by vagus nerve stimulation

Rodenkirch, C., & Wang, Q. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab6b84 · Cited by 21

Advanced Electrical and Optical Microsystems for Biointerfacing

Obaid, S. N., Chen, Z., & Lu, L. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000091 · Cited by 21

Population codes in the visual cortex

Tanabe, S. (2013) · Neuroscience Research · doi:10.1016/j.neures.2013.03.010 · Cited by 23

Feedback for reinforcement learning based brain–machine interfaces using confidence metrics

Prins, N. W., Sanchez, J. C., & Prasad, A. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa6317 · Cited by 24

Mapping of primary somatosensory cortex of the hand area using a high-density electrocorticography grid for closed-loop brain computer interface

Kramer, D. R., Lee, M. B., Barbaro, M. F., Gogia, A. S., Peng, T. D., Liu, C. Y., Kellis, S., & Lee, B. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab7c8e · Cited by 21

Effects of Optogenetic Stimulation of Primary Somatosensory Cortex and Its Projections to Striatum on Vibrotactile Perception in Freely Moving Rats

Sun, Z., Schneider, A., Alyahyay, M., Karvat, G., & Diester, I. (2021) · eNeuro · doi:10.1523/eneuro.0453-20.2021 · Cited by 22

Brain state limits propagation of neural signals in laminar cortical circuits

Kharas, N., Andrei, A. R., Debes, S. R., & Dragoi, V. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2104192119 · Cited by 21

A Brain to Spine Interface for Transferring Artificial Sensory Information

Yadav, A. P., Li, D., & Nicolelis, M. A. L. (2020) · Scientific Reports · doi:10.1038/s41598-020-57617-3 · Cited by 22

Variable weight neural networks and their applications on material surface and epilepsy seizure phase classifications

Lam, H., Ekong, U., Xiao, B. J., Ouyang, G., Liu, H., Chan, K. Y., & Ling, S. H. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.09.011 · Cited by 21

Patterned electrical brain stimulation by a wireless network of implantable microdevices

Lee, A., Lee, J.-H., Leung, V., Larson, L. E., & Nurmikko, A. V. (2024) · Nature Communications · doi:10.1038/s41467-024-54542-1 · Cited by 29

A Robust Encoding Scheme for Delivering Artificial Sensory Information via Direct Brain Stimulation

Bjånes, D. A., & Moritz, C. T. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2936739 · Cited by 20

Does brain activity cause consciousness? A thought experiment

Gidon, A., Aru, J., & Larkum, M. E. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001651 · Cited by 25

Emerging Medical Technologies and Their Use in Bionic Repair and Human Augmentation

Manero, A. C., Rivera, V., Fu, Q., Schwartzman, J. D., Gibbs, H. P., Shah, N., Gandhi, D. A., White, E., Crawford, K. E., & Coathup, M. J. (2024) · Bioengineering · doi:10.3390/bioengineering11070695 · Cited by 24

Learning active sensing strategies using a sensory brain–machine interface

Richardson, A. G., Ghenbot, Y. G., Liu, X., Hao, H., Rinehart, C., DeLuccia, S., Maldonado, S. T., Boyek, G., Zhang, M., Aflatouni, F., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1909953116 · Cited by 20

Novel intraoperative online functional mapping of somatosensory finger representations for targeted stimulating electrode placement: technical note

McMullen, D. P., Thomas, T. M., Fifer, M. S., Candrea, D. N., Tenore, F. V., Nickl, R. W., Pohlmeyer, E. A., Coogan, C. G., Osborn, L. E., Schiavi, A. J., et al. (2021) · Journal of neurosurgery · doi:10.3171/2020.9.jns202675 · Cited by 20

Tactile representation in somatosensory thalamus (VPL) and cortex (S1) of awake primate and the plasticity induced by VPL neuroprosthetic stimulation

Song, W., & Semework, M. (2015) · Brain Research · doi:10.1016/j.brainres.2015.08.046 · Cited by 17

Clinical neuroscience and neurotechnology: An amazing symbiosis

Cometa, A., Falasconi, A., Biasizzo, M., Carpaneto, J., Horn, A., Mazzoni, A., & Micera, S. (2022) · iScience · doi:10.1016/j.isci.2022.105124 · Cited by 21

An externally head-mounted wireless neural recording device for laboratory animal research and possible human clinical use

Yin, M., Li, H., Bull, C. W., Borton, D. A., Aceros, J. C., Larson, L. E., & Nurmikko, A. V. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610199 · Cited by 18

Extracellular Neural Microstimulation May Activate Much Larger Regions than Expected by Simulations: A Combined Experimental and Modeling Study

Joucla, S., Branchereau, P., Cattaert, D., & Yvert, B. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041324 · Cited by 19

Brain-Computer Interfaces in Quadriplegic Patients

Lee, M. B., Kramer, D. R., Peng, T. D., Barbaro, M. F., Liu, C. Y., Kellis, S., & Lee, B. (2019) · Neurosurgery Clinics of North America · doi:10.1016/j.nec.2018.12.009 · Cited by 20

The Potential Transformation of Our Species by Neural Enhancement

Zehr, E. P. (2015) · Journal of Motor Behavior · doi:10.1080/00222895.2014.916652 · Cited by 19

Manufacture and applications of GaN-based piezotronic and piezo-phototronic devices

Niu, J., Wang, J., Sha, W., Long, Y., Ma, B., & Hu, W. (2024) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/ad8732 · Cited by 18

A Decade of Haptic Feedback for Upper Limb Prostheses

Dey, A., Basumatary, H., & Hazarika, S. M. (2023) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2023.3310045 · Cited by 19

All-Organic Conductive Biomaterial as an Electroactive Cell Interface

Zhuang, A., Bian, Y., Zhou, J., Fan, S., Shao, H., Hu, X., Zhu, B., & Zhang, Y. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.8b13820 · Cited by 18

Adversarial filtering based evasion and backdoor attacks to EEG-based brain-computer interfaces

Meng, L., Jiang, X., Chen, X., Liu, W., Luo, H., & Wu, D. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102316 · Cited by 19

High-Side Digitally Current Controlled Biphasic Bipolar Microstimulator

Hanson, T., Omarsson, B., O’Doherty, J. E., Peikon, I. D., Lebedev, M., & Nicolelis, M. A. L. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2187219 · Cited by 17

Generating artificial sensations with spinal cord stimulation in primates and rodents

Yadav, A. P., Li, S., Krucoff, M. O., Lebedev, M., Abd‐El‐Barr, M. M., & Nicolelis, M. A. L. (2021) · Brain stimulation · doi:10.1016/j.brs.2021.04.024 · Cited by 17

Neurocontrol: Methods, models and technologies for manipulating dynamics in the brain

Ritt, J. T., & Ching, S. (2015) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2015.7171915 · Cited by 16

Interactions Between Tactile and Proprioceptive Representations in Haptics

Rincon-Gonzalez, L., Naufel, S. N., Santos, V. J., & Tillery, S. H. (2012) · Journal of Motor Behavior · doi:10.1080/00222895.2012.746281 · Cited by 21

Brain–machine–brain interfaces as the foundation for the next generation of neuroprostheses

Nicolelis, M. A. L. (2021) · National Science Review · doi:10.1093/nsr/nwab206 · Cited by 16

A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

Li, L., Brockmeier, A. J., Choi, J. S., Francis, J. T., Sanchez, J. C., & Príncipe, J. C. (2014) · Computational Intelligence and Neuroscience · doi:10.1155/2014/870160 · Cited by 16

Brain–machine interface for eye movements

Graf, A. B. A., & Andersen, R. A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419977111 · Cited by 15

Wearable electroencephalography technologies for brain–computer interfacing

Liyanage, S. R., & Bhatt, C. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815369-7.00003-3 · Cited by 16

The ‘NeuroGate’: neuromorphic intelligence, extended mind, and neurorights

Farina, M., & Lavazza, A. (2024) · Synthese · doi:10.1007/s11229-024-04795-6 · Cited by 18

Advances in flexible high-density microelectrode arrays for brain-computer interfaces

Ban, S., Chong, D., Kwon, J., Lee, S., Huang, Y., Yoo, S., & Yeo, W. (2025) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2025.118102 · Cited by 15

A Bidirectional Brain-Machine Interface Algorithm That Approximates Arbitrary Force-Fields

Vato, A., Szymanski, F. D., Semprini, M., Mussa-Ivaldi, F. A., & Panzeri, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091677 · Cited by 15

Multielectrode and Multitransistor Arrays for In Vivo Recording

Vassanelli, S. (2014) · Springer eBooks · doi:10.1007/978-1-4899-8038-0_8 · Cited by 15

Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces

Panzeri, S., Safaai, H., Feo, V. D., & Vato, A. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00165 · Cited by 14

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment

Li, Y., Sun, R., Wang, Y., Li, H., & Zheng, X. (2016) · PLoS ONE · doi:10.1371/journal.pone.0165600 · Cited by 18

Central nervous system microstimulation: Towards selective micro-neuromodulation

Urdaneta, M. E., Koivuniemi, A. S., & Otto, K. J. (2017) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2017.09.012 · Cited by 16

Preserved cortical somatotopic and motor representations in tetraplegic humans

Andersen, R. A., & Aflalo, T. (2022) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2022.102547 · Cited by 17

Cortical neuroprosthetics from a clinical perspective

Tsu, A., Burish, M. J., Godlove, J. M., & Ganguly, K. (2015) · Neurobiology of Disease · doi:10.1016/j.nbd.2015.07.015 · Cited by 16

Spatial Asymmetry in Tactile Sensor Skin Deformation Aids Perception of Edge Orientation During Haptic Exploration

Wong, R. D. P., Hellman, R. B., & Santos, V. J. (2013) · IEEE Transactions on Haptics · doi:10.1109/toh.2013.56 · Cited by 15

Reducing Behavioral Detection Thresholds per Electrode via Synchronous, Spatially-Dependent Intracortical Microstimulation

Kunigk, N. G., Urdaneta, M. E., Malone, I. G., Delgado, F., & Otto, K. J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.876142 · Cited by 14

Review of Neural Interfaces: Means for Establishing Brain–Machine Communication

Khuntia, P. K., & Manivannan, P. V. (2023) · SN Computer Science · doi:10.1007/s42979-023-02160-x · Cited by 15

Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback

Abbasi, A., Lassagne, H., Estebanez, L., Goueytes, D., Shulz, D. E., & Ego‐Stengel, V. (2023) · Science Advances · doi:10.1126/sciadv.adh1328 · Cited by 20

Customizable, self-healing, and biocompatible microLED-hydrogel integration displays

Niu, J., Wang, J., Shi, Y., Dong, Z., Huang, T., Dai, X., Sha, W., Long, Y., & Hu, W. (2024) · Nano Energy · doi:10.1016/j.nanoen.2024.110074 · Cited by 14

Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness

Kao, J. C., Nuyujukian, P., Stavisky, S. D., Ryu, S. I., Ganguli, S., & Shenoy, K. V. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6609495 · Cited by 13

A bidirectional brain-machine interface connecting alert rodents to a dynamical system

Boi, F., Semprini, M., Mussa-Ivaldi, F. A., Panzeri, S., & Vato, A. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318298 · Cited by 14

Psychophysical correspondence between vibrotactile intensity and intracortical microstimulation for tactile neuroprostheses in rats

Devecıoğlu, İ., & Güçlü, B. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2552/14/1/016010 · Cited by 14

Genetic Modulation at the Neural Microelectrode Interface: Methods and Applications

Winter, B. M., Daniels, S. R., Salatino, J. W., & Purcell, E. K. (2018) · Micromachines · doi:10.3390/mi9100476 · Cited by 14

PET Mapping for Brain–Computer Interface Stimulation of the Ventroposterior Medial Nucleus of the Thalamus in Rats with Implanted Electrodes

Zhu, Y., Xu, K., Xu, C., Zhang, J., Ji, J., Zheng, X., Zhang, H., & Tian, M. (2016) · Journal of Nuclear Medicine · doi:10.2967/jnumed.115.171868 · Cited by 14

Visual reconstruction from 2-photon calcium imaging suggests linear readout properties of neurons in mouse primary visual cortexPreprint

Garasto, S., Bharath, A. A., & Schultz, S. R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/300392 · Cited by 14

Enabling closed-source applications for virtual reality via OpenGL intercept-based techniques

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Large-scale multimodal surface neural interfaces for primates

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