Sensors and Decoding for Intracortical Brain Computer Interfaces

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Discrete Time Signal Processing

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Cognitive Control Signals for Neural Prosthetics

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Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects

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Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array

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Inference of hand movements from local field potentials in monkey motor cortex

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Brain-Computer Interfaces Using Electrocorticographic Signals

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Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia

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Cortical Local Field Potential Encodes Movement Intentions in the Posterior Parietal Cortex

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Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills

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Predicting Measures of Motor Performance from Multiple Cortical Spike Trains

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Silicon-substrate intracortical microelectrode arrays for long-term recording of neuronal spike activity in cerebral cortex

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A glass/silicon composite intracortical electrode array

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Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex

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Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkey

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An implantable wireless neural interface for recording cortical circuit dynamics in moving primates

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Encoding of Movement Direction in Different Frequency Ranges of Motor Cortical Local Field Potentials

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Functional network reorganization during learning in a brain-computer interface paradigm

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Predicting Movement from Multiunit Activity

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Poly(3,4-ethylenedioxythiophene) (PEDOT) polymer coatings facilitate smaller neural recording electrodes

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Primary Motor Cortex Tuning to Intended Movement Kinematics in Humans with Tetraplegia

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Decoding Complete Reach and Grasp Actions from Local Primary Motor Cortex Populations

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Recursive Bayesian Decoding of Motor Cortical Signals by Particle Filtering

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Intra-day signal instabilities affect decoding performance in an intracortical neural interface system

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Neurons in primary motor cortex engaged during action observation

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Single-unit neural recording with active microelectrode arrays

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Wireless Neural Recording With Single Low-Power Integrated Circuit

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Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials

Bansal, A. K., Truccolo, W., Vargas-Irwin, C. E., & Donoghue, J. P. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00781.2011 · Cited by 200 · see citing works

Accurate decoding of reaching movements from field potentials in the absence of spikes

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Extracting kinematic parameters for monkey bipedal walking from cortical neuronal ensemble activity

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A recurrent neural network for closed-loop intracortical brain–machine interface decoders

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Unscented Kalman Filter for Brain-Machine Interfaces

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

Zhuang, J., Truccolo, W., Vargas-Irwin, C. E., & Donoghue, J. P. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2047015 · Cited by 173 · see citing works

Sensorimotor transformations in cortical motor areas

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Closed-Loop Decoder Adaptation on Intermediate Time-Scales Facilitates Rapid BMI Performance Improvements Independent of Decoder Initialization Conditions

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Control of a brain–computer interface without spike sorting

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A 100-Channel Hermetically Sealed Implantable Device for Chronic Wireless Neurosensing Applications

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HermesD: A High-Rate Long-Range Wireless Transmission System for Simultaneous Multichannel Neural Recording Applications

Miranda, H. C., Gilja, V., Chestek, C. A., Shenoy, K. V., & Meng, T. H. Y. (2010) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2010.2044573 · Cited by 161 · see citing works

Detecting Neural-State Transitions Using Hidden Markov Models for Motor Cortical Prostheses

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Challenges and Opportunities for Next-Generation Intracortically Based Neural Prostheses

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Learning to move machines with the mind

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Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices

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Cortical Representation of Ipsilateral Arm Movements in Monkey and Man

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Solid-State Electrodes for Multichannel Multiplexed Intracortical Neuronal Recording

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Toward the Restoration of Hand Use to a Paralyzed Monkey: Brain-Controlled Functional Electrical Stimulation of Forearm Muscles

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HermesB: A Continuous Neural Recording System for Freely Behaving Primates

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Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain–computer interface algorithms

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HermesC: Low-Power Wireless Neural Recording System for Freely Moving Primates

Chestek, C. A., Gilja, V., Nuyujukian, P., Kier, R. J., Solzbacher, F., Ryu, S. I., Harrison, R. R., & Shenoy, K. V. (2009) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2023293 · Cited by 130 · see citing works

Adaptive Decoding for Brain-Machine Interfaces Through Bayesian Parameter Updates

Li, Z., O’Doherty, J. E., Lebedev, M., & Nicolelis, M. A. L. (2011) · Neural Computation · doi:10.1162/neco_a_00207 · Cited by 126 · see citing works

Superlinear Population Encoding of Dynamic Hand Trajectory in Primary Motor Cortex

Paninski, L., Shoham, S., Fellows, M. R., Hatsopoulos, N. G., & Donoghue, J. P. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.0919-04.2004 · Cited by 121 · see citing works

Factor-Analysis Methods for Higher-Performance Neural Prostheses

Santhanam, G., Yu, B. M., Gilja, V., Ryu, S. I., Afshar, A., Sahani, M., & Shenoy, K. V. (2009) · Journal of Neurophysiology · doi:10.1152/jn.00097.2009 · Cited by 112 · see citing works

Continuous neuronal ensemble control of simulated arm reaching by a human with tetraplegia

Chadwick, E. K., Blana, D., Simeral, J. D., Lambrecht, J. M., Kim, S. P., Cornwell, A. S., Taylor, D. M., Hochberg, L. R., Donoghue, J. P., & Kirsch, R. F. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/3/034003 · Cited by 110 · see citing works

Efficient Decoding With Steady-State Kalman Filter in Neural Interface Systems

Malik, W. Q., Truccolo, W., Brown, E. N., & Hochberg, L. R. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2010.2092443 · Cited by 98 · see citing works

Novel multi-sided, microelectrode arrays for implantable neural applications

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Kinetic Trajectory Decoding Using Motor Cortical Ensembles

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Population decoding of motor cortical activity using a generalized linear model with hidden states

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Real-Time Decoding of Nonstationary Neural Activity in Motor Cortex

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Brain Control of Movement Execution Onset Using Local Field Potentials in Posterior Parietal Cortex

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High Accuracy Decoding of Movement Target Direction in Non-Human Primates Based on Common Spatial Patterns of Local Field Potentials

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General-Purpose Filter Design for Neural Prosthetic Devices

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Decoding Individuated Finger Movements Using Volume-Constrained Neuronal Ensembles in the M1 Hand Area

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Signal Processing Challenges for Neural Prostheses

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Agency and Accountability: Ethical Considerations for Brain-Computer Interfaces.

Davidoff, E. J. (2020) · PubMed · Cited by 9 · see citing works

BrainGate: An Intracortical Brain-Computer Interface for the Restoration of Communication and Functional Independence for People with Paralysis

Rubin, D. B., & Hochberg, L. R. (2023) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci57258.2023.10078547 · Cited by 9 · see citing works

Enhanced magnetic transduction of neuronal activity by nanofabricated inductors quantified via finite element analysis

Phillips, J. K., Glodowski, M., Gokhale, Y., Dwyer, M. M., Ashtiani, A. O., & Hai, A. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac7907 · Cited by 9 · see citing works

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Schaeffer, M.-C., & Aksenova, T. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00540 · Cited by 9 · see citing works

Hierarchical Surface Restructuring of Ultra‐Thin Electrodes and Microelectrode Arrays for Neural Interfacing with Peripheral and Central Nervous Systems

Blagojevic, A., Seche, W., Choi, H., Davis, S. L., Elyahoodayan, S., Caputo, G. A., Lowe, T. C., Tavousi, P., Shahbazmohamadi, S., & Amini, S. (2024) · Advanced Materials Interfaces · doi:10.1002/admi.202400017 · Cited by 8 · see citing works

Neuromodulation methods for animal locomotion control

Cho, Y. K., Kim, S., Jung, H. H., Chang, J. W., Kim, Y.-J., Shin, H.-C., & Jun, S. B. (2016) · Biomedical Engineering Letters · doi:10.1007/s13534-016-0234-2 · Cited by 8 · see citing works

Intracortical Hindlimb Brain–Computer Interface Systems: A Systematic Review

Ghodrati, M. T., Mirfathollahi, A., Shalchyan, V., & Daliri, M. R. (2023) · IEEE Access · doi:10.1109/access.2023.3258969 · Cited by 8 · see citing works

Early detection of human focal seizures based on cortical multiunit activity

Park, Y. S., Hochberg, L. R., Eskandar, E. N., Cash, S. S., & Truccolo, W. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944945 · Cited by 8 · see citing works

A Novel and Simple Spike Sorting Implementation

Petrantonakis, P. C., & Poirazi, P. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2640858 · Cited by 8 · see citing works

Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex

Even-Chen, N., Stavisky, S. D., Kao, J. C., Ryu, S. I., & Shenoy, K. V. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318303 · Cited by 7 · see citing works

Using spike train distances to identify the most discriminative neuronal subpopulation

Satuvuori, E., Mulansky, M., Daffertshofer, A., & Kreuz, T. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2018.09.008 · Cited by 7 · see citing works

Compact and interpretable architecture for speech decoding from stereotactic EEG

Petrosyan, A., Voskoboynikov, A., & Ossadtchi, A. E. (2021) · International Conference Neurotechnologies and Neurointerfaces (CNN) · doi:10.1109/cnn53494.2021.9580381 · Cited by 7 · see citing works

Deep Learning for Natural Image Reconstruction from Electrocorticography Signals

Date, H., Kawasaki, K., Hasegawa, I., & Okatani, T. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983029 · Cited by 6 · see citing works

Dorsal Column Nuclei Neural Signal Features Permit Robust Machine-Learning of Natural Tactile- and Proprioception-Dominated Stimuli

Loutit, A. J., & Potas, J. R. (2020) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.00046 · Cited by 4 · see citing works

Brain–Computer Interfaces

Donoghue, J. P. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-805353-9.00025-5 · Cited by 5 · see citing works

In vivo electrophysiology

Aria, M. M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817070-0.00004-x · Cited by 4 · see citing works

A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography

Yeom, H. G., Hong, W., Kang, D., Chung, C. K., Kim, J. S., & Kim, S.-P. (2014) · BioMed Research International · doi:10.1155/2014/176857 · Cited by 4 · see citing works

Implantable Neural Sensors for Brain Machine Interface

Jang, J., Lee, J.-H., Kang, M., & Song, Y. (2015) · KAIST research series · doi:10.1007/978-94-017-9981-2_3 · Cited by 3 · see citing works

Extracellular voltage thresholds for maximizing information extraction in primate auditory cortex: implications for a brain computer interface

Bigelow, J., & Malone, B. J. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab7c19 · Cited by 3 · see citing works

Neural Prostheses

Kasten, M. R., Ievins, A., & Moritz, C. T. (2015) · Wiley eBooks · doi:10.1002/9780470015902.a0024011 · Cited by 3 · see citing works

A large electroencephalogram database of freewill reaching and grasping tasks for brain machine interfaces

Thapa, B. R., Boggess, J., & Bae, J. (2025) · Scientific Data · doi:10.1038/s41597-025-06039-9 · Cited by 3 · see citing works

A Neuroprosthetic for Individuals with Tetraplegia: The Path from a Clinical Research Tool to a Home-Use Assistive Device

Colachis, S. C., Ganzer, P. D., Friedenberg, D. A., & Sharma, G. (2021) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_119-1 · Cited by 2 · see citing works

Robust Online Estimation of Biophysical Neural Circuits

Schmetterling, R., Burghi, T. B., & Sepulchre, R. J. (2023) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc49753.2023.10383582 · Cited by 2 · see citing works

The brain as site-specific surveillant performative space

Pearlman, E. (2015) · International Journal of Performance Arts and Digital Media · doi:10.1080/14794713.2015.1084810 · Cited by 2 · see citing works

Heterogeneous domain adaptation for intracortical signal classification using domain consensus

Dong, Y., Hu, D., Wang, S., & He, J. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104540 · Cited by 2 · see citing works

Decoding neural signals and discovering their representations with a compact and interpretable convolutional neural networkPreprint

Petrosuan, A., Lebedev, M., & Ossadtchi, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.02.129114 · Cited by 2 · see citing works

Brain-Computer Interfaces for Rehabilitation After Stroke

Ganguly, K. (2024) · Springer eBooks · doi:10.1007/978-3-031-49963-0_27 · Cited by 1 · 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

Review: Human Intracortical Recording and Neural Decoding for Brain–Computer Interfaces

Brandman, D. M., Cash, S. S., & Hochberg, L. R. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2677443 · Cited by 102 · see citing works

BrainGate: An Intracortical Brain-Computer Interface for the Restoration of Communication and Functional Independence for People with Paralysis

Rubin, D. B., & Hochberg, L. R. (2023) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci57258.2023.10078547 · Cited by 9 · see citing works

Advances in Invasive Brain–Computer Interface Technology and Decoding Methods for Restoring Movement and Future Applications

Bouton, C. E. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-805353-9.00029-2 · Cited by 3 · see citing works

The state-of-the-art of invasive brain-computer interfaces in humans: a systematic review and individual patient meta-analysis

Lim, M. J. R., Lo, Y. T., Tan, Y. Y., Lin, H.-Y., Wang, Y., Tan, D., Wang, E., Ma, Y. Y. N., Ng, J. J. W., Jefree, R. A., et al. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adb88e · Cited by 16 · see citing works