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Wireless Neural Recording With Single Low-Power Integrated Circuit

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WIMAGINE: Wireless 64-Channel ECoG Recording Implant for Long Term Clinical Applications

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256-Channel Neural Recording and Delta Compression Microsystem With 3D Electrodes

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Decoding 3-D Reach and Grasp Kinematics From High-Frequency Local Field Potentials in Primate Primary Motor Cortex

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Neural Dust: An Ultrasonic, Low Power Solution for Chronic Brain-Machine Interfaces

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A Fully-Integrated, Miniaturized (0.125 mm²) 10.5 µW Wireless Neural Sensor

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A 128-Channel 6mW Wireless Neural Recording IC with On-the-Fly Spike Sorting and UWB Tansmitter

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Glycine-spacers influence functional motifs exposure and self-assembling propensity of functionalized substrates tailored for neural stem cell cultures

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Intracortical recording stability in human brain–computer interface users

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A new high-density (25 electrodes/mm²) penetrating microelectrode array for recording and stimulating sub-millimeter neuroanatomical structures

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A Low-Power 32-Channel Digitally Programmable Neural Recording Integrated Circuit

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Roles of Monkey Premotor Neuron Classes in Movement Preparation and Execution

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A Low-Power, High CMRR Neural Amplifier System Employing CMOS Inverter-Based OTAs With CMFB Through Supply Rails

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Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals

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Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas

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A 75-µW, 16-Channel Neural Spike-Sorting Processor With Unsupervised Clustering

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Selection and parameterization of cortical neurons for neuroprosthetic control

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Rapid control and feedback rates enhance neuroprosthetic control

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An implantable 700μW 64-channel neuromodulation IC for simultaneous recording and stimulation with rapid artifact recovery

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Asynchronous Decoding of Dexterous Finger Movements Using M1 Neurons

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Modular 128-Channel $\Delta$ - $\Delta \Sigma$ Analog Front-End Architecture Using Spectrum Equalization Scheme for 1024-Channel 3-D Neural Recording Microsystems

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Local field potentials allow accurate decoding of muscle activity

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A fully implantable 96-channel neural data acquisition system

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Performance measurement for brain–computer or brain–machine interfaces: a tutorial

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915-MHz FSK/OOK Wireless Neural Recording SoC With 64 Mixed-Signal FIR Filters

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A High-Performance Neural Prosthesis Incorporating Discrete State Selection With Hidden Markov Models

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Model-Based Development of Neural Prostheses for Movement

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A Low Power Light Weight Wireless Multichannel Microsystem for Reliable Neural Recording

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Enabling Low-Power, Multi-Modal Neural Interfaces Through a Common, Low-Bandwidth Feature Space

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A 1V 0.25uW inverter-stacking amplifier with 1.07 noise efficiency factor

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A Low-Power 0.7 $\mu {\rm V_{rms}}$ 32-Channel Mixed-Signal Circuit for ECoG Recordings

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A 330μm×90μm opto-electronically integrated wireless system-on-chip for recording of neural activities

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Recording nerve signals in canine sciatic nerves with a flexible penetrating microelectrode array

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

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Multi-scale recordings for neuroprosthetic control of finger movements

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A bidirectional neural interface SoC with an integrated spike recorder, microstimulator, and low-power processor for real-time stimulus artifact rejection

Limnuson, K., Lu, H., Chiel, H. J., & Mohseni, P. (2015) · Analog Integrated Circuits and Signal Processing · doi:10.1007/s10470-015-0489-z · Cited by 15 · see citing works

A 64-channel ultra-low power bioelectric signal acquisition system for brain-computer interface

Mahajan, A., Karimi-Bidhendi, A., Wang, P. T., McCrimmon, C. M., Liu, C. Y., Nenadić, Z., Do, A. H., & Heydari, P. (2015) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2015.7348355 · Cited by 14 · see citing works

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 · see citing works

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 · see citing works

Miniature battery-free bioelectronics

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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 · see citing works

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 · see citing works

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 · see citing works

Multimodal monitoring of human cortical organoids implanted in mice reveal functional connection with visual cortex

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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 · see citing works

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 88 · see citing works

The science and engineering behind sensitized brain-controlled bionic hands

Pandarinath, C., & Bensmaı̈a, S. J. (2021) · Physiological Reviews · doi:10.1152/physrev.00034.2020 · Cited by 76 · see citing works

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 81 · see citing works

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 · see citing works

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 · see citing works

Ultra-small carbon fiber electrode recording site optimization and improved in vivo chronic recording yield

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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 61 · see citing works

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 53 · see citing works

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 52 · see citing works

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 · see citing works

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 · see citing works

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 41 · see citing works

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 42 · see citing works

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 · see citing works

Brain–Machine Interfaces: Closed-Loop Control in an Adaptive System

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Brain–computer interfaces for neuropsychiatric disorders

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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 52 · see citing works

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 · see citing works

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 · see citing works

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 34 · see citing works

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 · see citing works

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 · see citing works

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 29 · see citing works

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 29 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 20 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 17 · see citing works

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 · see citing works

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 15 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Decoding pain from brain activity

Chen, Z. S. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac28d4 · Cited by 12 · see citing works

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 · see citing works

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 11 · see citing works

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 10 · see citing works

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 9 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 5 · see citing works

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 · see citing works

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 · see citing works

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 10 · see citing works

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 · see citing works

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 5 · see citing works

A machine learning approach to characterize sequential movement-related states in premotor and motor cortices

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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 · see citing works

Progressive Retinal Degeneration Increases Cortical Response Latency of Light Stimulation but Not of Electric Stimulation

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Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methodsPreprint

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Multimodal monitoring of human cortical organoids implanted in mice using transparent graphene microelectrodes reveal functional connection between organoid and mouse visual cortexPreprint

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State-Dependent Neural Decoding of Complex Handwriting via Mixture of Experts With Recurrent Routing

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