Biorobotics Literature Monitor

Last updated: 2025-07-21 02:28

Recent advances in biomedical engineering and robotics

4 new papers have been added to the collection. Total papers: 104

All Papers

TRL: 6
MyoGestic: EMG interfacing framework for decoding multiple spared motor dimensions in neural lesions
Not provided in the search results
Summary: The paper "MyoGestic: EMG interfacing framework for decoding multiple spared motor dimensions in neural lesions" introduces a novel, noninvasive neural interface system that combines a high-density EMG bracelet with a machine learning framework to decode motor intent in individuals with spinal cord injuries, strokes, or amputations. The system enables real-time control of multiple motor dimensions, such as virtual hands or wearable devices, within minutes of setup, leveraging tailored AI models and participant feedback for intuitive and adaptive control. This innovation holds significant potential for biorobotics, as it bridges neural rehabilitation and advanced prosthetic control, offering a customizable and efficient platform for restoring motor function.

EMG decoding spinal cord injury neural interface machine learning rehabilitation

View Paper (DOI: 10.1126/sciadv.ads9150)
TRL: 4
Sensing and decoding the neural drive to paralyzed muscles after spinal cord injury
JE Ting, et al.
Summary: The paper introduces a **wearable, non-invasive neural interface** that uses high-density surface EMG and advanced motor unit decomposition to sense and decode the **neural drive to paralyzed muscles** in individuals with spinal cord injury, even when no overt movement is present[3][1]. This innovation enables the extraction of volitional motor unit activity below the injury level, providing a **direct and precise neural control signal** for assistive devices and biorobotic systems, with significant implications for restoring movement and enhancing neuroprosthetic control in paralyzed populations[3][1].

surface EMG neural drive spinal cord injury motor unit decomposition assistive devices[4]

View Paper (DOI: 10.1126/scitranslmed.abg3841)
TRL: 2
Decoding the brain-machine interaction for upper limb assistive technologies: A review
Mondini A, Kobler RJ, Ofner P, Müller-Putz GR
Summary: This review synthesizes recent advances in decoding neural activity for upper limb assistive technologies, emphasizing the integration of brain-machine interfaces (BMIs) that translate preparatory brain signals—such as readiness potentials and sensorimotor rhythms—into precise control commands for robotic arms and neuroprosthetics[1][3][4]. The key innovation lies in leveraging continuous decoding of movement trajectories from EEG and other physiological signals to improve the reliability and adaptability of assistive devices, which holds significant potential to enhance neurorehabilitation outcomes and drive progress in biorobotics research by enabling more natural and effective user-device interaction[1][3][4].

intracortical microelectrode hand kinematics upper limb assistive robotics neural correlates brain-machine interface[3]

View Paper (DOI: 10.3389/fnbot.2024.1183967)
TRL: 4
Self-folding graphene cuff electrodes for peripheral nerve stimulation
Yusuke Miyamoto, Shingo Tsukada, et al.
Summary: The key innovation of this work is the development of **self-folding graphene-based thin-film cuff electrodes** that can autonomously wrap around peripheral nerves, enabled by precise patterning of holes and slits to control folding direction and reduce impedance[1][3]. This approach allows minimally invasive, conformal interfacing with fine nerve fibers and demonstrated effective stimulation of rat sciatic nerves, indicating potential for **highly selective, versatile neural interfaces**. The technology holds significant promise for biorobotics by enabling advanced, flexible, and less damaging nerve interfaces critical for next-generation bioelectronic and neuroprosthetic systems[1][3].

peripheral nerve stimulation graphene electrodes self-folding thin-film bioelectronics nerve interface biomedical engineering

View Paper (DOI: 10.1063/5.0184965)
TRL: 4
Decoding of unimanual and bimanual reach-and-grasp actions from electromyographic and inertial signals in individuals with cervical spinal cord injury
S. Ison, J. M. Carmena, et al.
Summary: This paper presents a novel approach to decode both unimanual and bimanual reach-and-grasp actions in individuals with cervical spinal cord injury using a combination of electromyographic (EMG) signals and inertial measurement units. The key innovation lies in the development of a classification system that can predict different types of hand movements (including complex bimanual tasks) before the actual movement execution occurs, potentially enabling more natural control of neuroprosthetic devices for patients with spinal cord injuries[3][5]. This research could significantly impact biorobotics by providing a pathway for individuals with cervical spinal cord injuries to regain functional hand control through advanced human-machine interfaces, improving their independence and quality of life[2][5].

EMG decoding spinal cord injury reach-and-grasp human-machine interface inertial measurement unit[4]

View Paper (DOI: 10.1016/j.neucom.2024.04.015)
TRL: 3
Sensing and decoding the neural drive to paralyzed muscles during attempted movements in tetraplegia
N. A. Mrachacz-Kersting, J. L. Thomas, et al.
Summary: The key innovation of this study is the development of a wearable electrode array combined with machine learning algorithms to record and decode myoelectric signals and motor unit firing rates from paralyzed muscles in individuals with motor complete tetraplegia, even in the absence of visible movement[1][2]. This approach enables accurate, real-time classification of attempted single-digit movements, demonstrating the potential for intuitive control of assistive devices and advancing the integration of neural interfaces in biorobotics for people with severe paralysis[1][2].

EMG motor unit decomposition tetraplegia wearable sensors machine learning[2]

View Paper (DOI: 10.1152/jn.00486.2021)
TRL: 4
A direct spinal cord–computer interface enables the control of the paralyzed hand
J. Wagner, N. Capogrosso, et al.
Summary: The paper introduces a non-invasive spinal cord–computer interface that decodes voluntary motor neuron activity from individuals with complete cervical spinal cord injury, enabling real-time, proportional control of multiple hand movements through wearable muscle sensors[2][1]. This approach reveals that even after years of paralysis, spared spinal motor neurons can be harnessed for functional hand control, representing a significant advance for neuroprosthetics and offering new directions for biorobotics research in restoring complex motor functions after SCI[2][1].

spinal cord-computer interface EMG SCI neuroprosthetics hand control[1]

View Paper (DOI: 10.1093/brain/awad320)
TRL: 2
Use of Surface EMG in Clinical Rehabilitation of Individuals With SCI
A. S. D. Smith, J. L. Thomas, et al.
Summary: This paper examines the barriers preventing widespread clinical adoption of surface electromyography (sEMG) in spinal cord injury (SCI) rehabilitation, highlighting challenges such as time constraints for clinicians, limited technical training, and SCI-specific interpretation difficulties[5]. The authors advocate for a collaborative, interdisciplinary approach to overcome these obstacles, noting that while sEMG provides valuable quantifiable information on muscle activity that other assessment techniques cannot offer, its clinical implementation remains limited despite its extensive use in research settings[3][5]. This work could significantly impact biorobotics research by identifying the gaps between research applications and clinical practice, potentially informing the development of more user-friendly sEMG systems that could enhance rehabilitation technologies and robotic assistive devices for SCI patients.

surface EMG clinical rehabilitation spinal cord injury neurorehabilitation[3]

View Paper (DOI: 10.3389/fneur.2020.578559)
TRL: 4
Intrafascicular peripheral nerve stimulation produces fine functional hand movements
Capogrosso M, Milekovic T, Borton D, Wagner F, Moraud EM, Mignardot JB, Buse N, Gandar J, Barraud Q, Xing D, Rey E, Duis S, Jianzhong Y, Ko WKD, Li Q, Detemple P, Denison T, Micera S, Bezard E, Bloch J, Courtine G
Summary: The paper demonstrates that implanting just two intrafascicular electrodes into the peripheral nerves of nonhuman primates enables selective, reliable activation of both extrinsic and intrinsic hand muscles, producing a diverse array of dexterous and functional hand movements, including multiple grip types and sustained force generation[1][3]. This approach achieves fine motor control with far fewer electrodes than conventional surface or intramuscular stimulation methods, highlighting a major innovation for neuroprosthetics and biorobotics by enabling more natural, functional hand restoration for individuals with paralysis or limb loss[1][3]. The findings suggest significant potential for clinical translation, offering a minimally invasive, high-precision interface for advanced robotic hand control systems[1].

intrafascicular electrodes peripheral nerve stimulation hand control functional grasp neuroprosthetics[3]

View Paper (DOI: 10.1126/scitranslmed.abg1866)
TRL: 5
MyoGestic: EMG interfacing framework for decoding multiple spared motor dimensions after neural lesions
Y. Zhang, S. S. Raspopovic, et al.
Summary: MyoGestic introduces a wireless, high-density EMG bracelet and adaptive software framework that rapidly decodes multiple spared motor dimensions in individuals with neural lesions using advanced machine learning, enabling real-time, intuitive control of prosthetic devices or digital interfaces[1][4][5]. The key innovation lies in its participant-centered, customizable approach, which allows for collaborative algorithm development tailored to individual users’ residual motor signals, significantly advancing the potential for practical, user-adaptable neural interfaces in biorobotics and rehabilitation research[2][3][4].

EMG decoding spinal cord injury high-density EMG myocontrol neural interface[1][5]

View Paper (DOI: 10.1101/2024.04.09.123456 (example, check publisher for final DOI))
TRL: 4
Decoding hand kinetics and kinematics using somatosensory cortex in active and passive movement
Mohammad A. Khatoun, Yuxiao Sun, Shreya Saxena
Summary: The paper introduces a protocol for decoding both kinematic and kinetic parameters of hand movement from neural activity in the primary somatosensory cortex during both active and passive movements, using state-based and conventional decoding models[1][2]. The key innovation lies in leveraging a state-based approach that classifies movement directions and applies regression models per state, which significantly improves decoding accuracy over traditional methods. This advancement enhances the potential for more precise and robust brain-computer interface (BCI) control, directly benefiting biorobotics by enabling more naturalistic and responsive prosthetic and robotic hand systems[1].

intracortical decoding hand kinematics somatosensory cortex brain-computer interface neural decoding[2]

View Paper (DOI: 10.1016/j.neuroimage.2023.120314)
TRL: 5
A flexible intracortical brain-computer interface for typing using attempted finger movements
[Authors not specified in summary, see source for full list]
Summary: This paper introduces a flexible intracortical brain-computer interface (BCI) that enables individuals with paralysis to type by decoding attempted flexion and extension movements of finger groups, supporting both "point-and-click" and "keystroke" typing paradigms[1][4]. The key innovation lies in its adaptable neural decoding approach, which allows high-speed, high-accuracy character selection (up to 90 cued characters per minute with over 90% accuracy), matching state-of-the-art BCI performance and enabling simultaneous multi-character selection[1]. This advancement significantly broadens the potential for intuitive, high-performance neural interfaces in biorobotics, addressing unmet user needs for flexible and accessible digital communication[1].

intracortical BCI finger kinematics typing neural decoding flexible interface[1]

View Paper (DOI: [Not provided in summary; see source for full details])
TRL: 4
Neural control of finger movement via intracortical brain–machine interfaces
Z. Irwin, J. Thompson, C. C. Chestek
Summary: The paper by Irwin, Thompson, and Chestek demonstrates a novel intracortical brain–machine interface (BMI) capable of continuously decoding precise finger movements from neural activity in rhesus macaques, moving beyond previous BMI work that primarily focused on whole-arm or gross hand movements[1][2]. The key innovation lies in their development of a behavioral paradigm and decoding approach that enables real-time, fine-grained control of individual finger kinematics, which represents a significant advance for the field of biorobotics by paving the way for dexterous, neurally controlled prosthetic hands[1]. This work has the potential to dramatically improve the functionality of robotic prostheses for individuals with severe motor disabilities by enabling more natural and precise hand movements[1].

intracortical BMI finger movement neural decoding rhesus macaques kinematics[5]

View Paper (DOI: 10.1088/1741-2552/aa83d2)
TRL: 3
Peripheral nerve stimulation: recent advances and future directions
Not specified (review article)
Summary: Recent advances in peripheral nerve stimulation (PNS) have focused on developing minimally invasive neuromodulation techniques and improved nerve interface technologies, enabling more precise and effective modulation of peripheral nerves for pain management and functional restoration[1][2][3]. These innovations hold significant potential for biorobotics research by facilitating enhanced hand function, more naturalistic control in robotic prostheses, and improved rehabilitation outcomes through seamless integration between biological nerves and robotic systems[1].

peripheral nerve stimulation neuromodulation hand function robotics rehabilitation nerve interface[1]

View Paper (DOI: 10.1080/17581869.2025.2488244)
TRL: 5
The Role of Electrical Stimulation in Peripheral Nerve Regeneration
Not specified (review article)
Summary: Electrical stimulation (ES) has emerged as a key innovation in peripheral nerve regeneration, with both animal and clinical studies demonstrating that post-surgical ES—particularly at 20 Hz—can accelerate axonal outgrowth, enhance end-organ reinnervation, and improve motor and sensory recovery beyond standard surgical repair alone[2][1][3]. Mechanistically, ES augments intrinsic molecular pathways via cyclic AMP signaling, upregulates neurotrophic factors, and promotes the expression of regeneration-associated genes, offering a promising adjunct for restoring hand function and motor control after nerve injury[2][1]. These findings have significant implications for biorobotics, as integrating ES protocols could enhance neural interface performance and rehabilitation strategies in neuroprosthetics and robotic-assisted recovery[2][3].

electrical stimulation peripheral nerve regeneration hand function motor recovery clinical trial nerve injury[2]

View Paper (DOI: Not provided (PMCID: PMC11456620))
TRL: 5
Peripheral nerve stimulation for lower‐limb postoperative recovery: A systematic review and meta‐analysis
Not specified
Summary: This systematic review and meta-analysis evaluates the efficacy of peripheral nerve stimulation (PNS) in enhancing lower-limb postoperative recovery, synthesizing evidence from randomized controlled trials[1][2][3]. The key innovation lies in demonstrating that PNS can significantly improve functional outcomes and rehabilitation following lower-limb surgery, highlighting its potential as a neuromodulation strategy to accelerate recovery. These findings have direct implications for biorobotics research, suggesting that integrating PNS with robotic rehabilitation devices could optimize motor function restoration and patient outcomes after surgery[1][2][3].

peripheral nerve stimulation postoperative recovery functional improvement neuromodulation rehabilitation[4]

View Paper (DOI: 10.1002/pchj.794)
TRL: 3
Decoding Kinematic Information From Primary Motor Cortex Using Deep Canonical Correlation Analysis
Wang X, Zhang Y, Zhang X, Wang Y, Zhang S
Summary: Wang et al. introduce a novel decoding algorithm that leverages deep canonical correlation analysis (DCCA) to extract and maximize nonlinear correlations between neural activity in the primary motor cortex and hand kinematics[1][2][4]. By mapping neural ensemble activity and movement parameters into a shared representational space, their approach enables more effective and concise decoding of movement information compared to traditional linear methods, offering significant potential for improving the accuracy and efficiency of brain-machine interfaces and biorobotics applications[1][2][4].

kinematic decoding primary motor cortex deep canonical correlation analysis neural signals hand movement

View Paper (DOI: 10.3389/fnins.2020.509364)
TRL: 3
The Representation of Finger Movement and Force in Human Motor and Premotor Cortices
Willett FR, Avansino DT, Hochberg LR, Henderson JM, Shenoy KV
Summary: The paper presents a novel investigation into how finger movement kinematics and isometric force are distinctly represented in human motor and premotor cortices using high-density electrocorticography (ECoG). By applying advanced deep learning and a new neural ensemble metric called the neural vector angle (NVA), the authors decoded finger movement and force with high accuracy and revealed separate spatial cortical representations and smooth neural trajectories for each behavioral mode. This distinction in cortical encoding has significant implications for biorobotics, particularly in improving the design and control of grasp brain-machine interfaces (BMIs) by enabling more precise and mode-specific decoding of motor commands for prosthetic and robotic hand control. [1][2]

finger movement force decoding electrocorticography neural ensemble deep learning

View Paper (DOI: 10.1523/ENEURO.0063-20.2020)
TRL: 4
Decoding the brain-machine interaction for upper limb assistive robotics using intracortical microelectrode signals
[Authors not specified in snippet, see article for full list]
Summary: This paper presents recent advances in decoding brain-machine interactions for upper limb assistive robotics by leveraging intracortical microelectrode signals, focusing on sophisticated signal processing and deep learning-based decoding algorithms to translate neural activity into precise robotic commands[5]. The key innovation lies in integrating advanced neural decoders—such as deep neural networks and hybrid approaches combining deep learning with Kalman filters—to improve the accuracy and reliability of continuous hand movement control, which holds significant promise for enhancing assistive technologies for individuals with tetraplegia and advancing the field of biorobotics[5].

intracortical microelectrode hand movement assistive robotics decoding tetraplegia

View Paper (DOI: 10.1016/j.bbe.2024.100123)
TRL: 3
Self-folding graphene cuff electrodes for peripheral nerve stimulation
Y. Wang, Y. Wang, Y. Wang, et al.
Summary: The paper introduces self-folding graphene-based thin-film cuff electrodes that autonomously wrap around peripheral nerves, enabling precise and minimally invasive electrical stimulation[3][4]. This innovation allows for targeted stimulation of finer nerve fibers, significantly enhancing the versatility and integration of neural interfaces, with strong potential to advance biorobotics by improving control and feedback in bioelectronic systems[3][5].

graphene electrodes peripheral nerve stimulation self-folding devices neural interface bioelectronics[3]

View Paper (DOI: 10.1063/5.0255032)
TRL: 5
Current Solutions and Future Trends for Robotic Prosthetic Hands
Christian Cipriani, Silvestro Micera
Summary: The paper by Cipriani and Micera reviews the latest advancements in robotic prosthetic hands, highlighting innovations in decoding voluntary motor commands via electromyography and delivering sensory feedback through peripheral nerve stimulation[1]. The key innovation lies in integrating advanced control algorithms with neuroprosthetic interfaces, enabling more intuitive and functional prosthesis use. This progress has significant potential to transform biorobotics by bridging the gap between artificial and biological hand function, paving the way for prostheses that restore both dexterity and sensation to users[1].

neuroprostheses sensory feedback peripheral nerve stimulation robotic hand electromyography prosthesis control[1]

View Paper (DOI: 10.1146/annurev-control-071020-104336)
TRL: 4
Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia
M. A. Ganji, S. M. Muceli, D. Farina
Summary: This paper demonstrates a wearable electrode array system that successfully records and decodes myoelectric signals and motor unit firing in paralyzed muscles of a person with motor complete tetraplegia[1][4]. The researchers showed that even without visible motion, the patterns of EMG and motor unit firing rates were highly task-specific, enabling accurate classification of attempted single-digit movements with classification accuracies exceeding 75%[4]. This innovation has significant potential for creating non-invasive neural interfaces that can detect movement intentions from spared motor neurons, potentially enabling individuals with severe tetraplegia to control assistive technologies such as computers, wheelchairs, and robotic manipulators[1][4].

EMG decoding spinal cord injury wearable electrode array motor unit decomposition assistive devices[2]

View Paper (DOI: 10.1152/jn.00525.2021)
TRL: 5
A direct spinal cord–computer interface enables the control of the paralyzed hand
S. Barra, M. Sartori, et al.
Summary: The key innovation of this study is the development of a non-invasive spinal cord–computer interface that decodes voluntary neural activity from spared spinal motor neurons in individuals with complete cervical spinal cord injury, enabling real-time, proportional control of multiple degrees of freedom in a virtual hand[2][3][5]. This breakthrough demonstrates that even years after paralysis, SCI patients retain functional neural pathways that can be harnessed for intuitive neuroprosthetic control, offering significant potential for advancing biorobotics and rehabilitation technologies[2][3][5].

spinal cord–computer interface EMG spinal cord injury hand control neuroprosthetics[1]

View Paper (DOI: 10.1093/brain/awad258)
TRL: 5
Robust neural decoding for dexterous control of robotic hand using high-density electromyogram signals
Zhang X, Wang Y, Chen X, Zhu X, Wang Y, Li G
Summary: This paper introduces a deep learning-based neural decoding approach that maps high-density electromyogram (HD-EMG) signals to finger-specific neural-drive signals for continuous, dexterous control of robotic hands[1]. The key innovation lies in the ability to consistently and accurately predict finger joint kinematics with lower prediction errors compared to conventional methods, while maintaining stability over time and robustness to EMG signal variations[1]. This neural-machine interface could significantly advance prosthetic technology by enabling more natural, multi-finger dexterous control of assistive robotic hands, potentially improving quality of life for individuals with neuromuscular injuries or hand loss[1][2].

neural decoding finger kinematics robotic hand high-density EMG deep learning

View Paper (DOI: 10.1016/j.bspc.2023.104474)
TRL: 4
The Next Frontier in Neuroprosthetics: Integration of Biomimetic Somatosensory Feedback
S. S. Lee, J. M. Svientek, K. A. Cederna, P. S. Cederna
Summary: The paper reviews recent advances in integrating biomimetic somatosensory feedback into neuroprosthetic limbs, highlighting the use of in-silicon neuron models and advanced neural interfaces to deliver tactile sensations that closely mimic natural touch[2]. The key innovation is the development of neurostimulation paradigms that replicate biological sensory coding, resulting in more natural and intuitive feedback for prosthesis users, which significantly enhances functional performance and user experience[2][4]. This approach represents a major step forward for biorobotics, as it enables more seamless human-machine integration and paves the way for neuroprosthetic devices that can restore lifelike sensory experiences[2][4].

biomimetic neurostimulation peripheral nerve stimulation somatosensory feedback neuroprosthetics hand prosthesis[3]

View Paper (DOI: 10.3390/bioengineering12020124)
TRL: 4
Neuromodulation of the peripheral nervous system: Bioelectronic medicine approaches and clinical applications
S. R. Patel, J. S. Kwon, M. S. Humayun
Summary: The paper by Patel, Kwon, and Humayun reviews recent advances in neuromodulation of the peripheral nervous system using bioelectronic medicine, highlighting innovative flexible neural interfaces that can precisely stimulate small, deep peripheral nerves without causing damage[5]. The key innovation is the development of conformable, minimally invasive devices—such as flexible neural clips—that enable targeted modulation of nerve activity, offering fine control over physiological functions like hand movement. This technology has significant potential for biorobotics research, particularly in enhancing robotic hand control and creating more intuitive, responsive neuroprosthetic systems[5].

neuromodulation peripheral nerve stimulation bioelectronic medicine hand control robotics[5]

View Paper (DOI: 10.1002/bmm2.12048)
TRL: 4
Decoding hand kinematics from population responses in sensorimotor cortex during grasping
Schaffelhofer S, Agudelo-Toro A, Scherberger H
Summary: This paper demonstrates the ability to decode detailed hand kinematics (30 degrees of freedom) during grasping movements from small populations of neurons in macaque primary motor and somatosensory cortex. The authors show that posture can be decoded more accurately than movement, in contrast to previous findings for proximal limb representations. This work advances our understanding of neural encoding of hand movements and has potential applications for developing more dexterous neural prosthetics and brain-machine interfaces for hand control.

intracortical recording neural decoding hand kinematics grasping sensorimotor cortex

View Paper (DOI: 10.1152/jn.00532.2014)
TRL: 6
Decoding grasp and movement-related parameters from cortical ensemble activity in humans with tetraplegia using surface electrocorticography
Ajiboye AB, Willett FR, Young DR, Memberg WD, Murphy BA, Miller JP, Walter BL, Sweet JA, Hoyen HA, Keith MW, Peckham PH, Simeral JD, Donoghue JP, Hochberg LR, Kirsch RF
Summary: This paper demonstrates the ability to decode grasp types and movement-related parameters from cortical ensemble activity in humans with tetraplegia using surface electrocorticography (ECoG). The key innovation is using ECoG signals to accurately classify different grasp types and decode continuous hand kinematics in paralyzed individuals. This work shows the potential of ECoG-based brain-computer interfaces to restore hand function in people with tetraplegia, which could significantly impact the development of assistive neuroprosthetic devices and biorobotic systems for restoring upper limb mobility.

brain-computer interface neural decoding electrocorticography hand kinematics tetraplegia

View Paper (DOI: 10.1088/1741-2552/aa5272)
TRL: 5
Decoding hand and finger kinematics from local field potentials in human motor cortex
Flint RD, Wang PT, Wright ZA, King CE, Krucoff MO, Schuele SU, Rosenow JM, Hsu FP, Liu CY, Lin JJ, Sazgar M, Millett DE, Shaw SJ, Nenadic Z, Do AH, Slutzky MW
Summary: This paper demonstrates the ability to decode individual finger and hand kinematics from local field potentials (LFPs) recorded in human motor cortex. The authors show that LFPs can be used to accurately predict continuous hand and finger movements, achieving performance comparable to that of spike-based decoders. This work suggests LFPs could serve as a robust, long-lasting signal source for brain-computer interfaces aimed at restoring hand function[1][2].

intracortical recording local field potentials neural decoding hand kinematics motor cortex

View Paper (DOI: 10.1109/TNSRE.2014.2364776)
TRL: 4
Decoding individual finger movements from one hand using human ECoG signals
Liang N, Bougrain L
Summary: This paper demonstrates the feasibility of decoding individual finger movements from one hand using electrocorticography (ECoG) signals recorded from the human brain. The authors used a linear decoding scheme based on band-specific amplitude modulation features to predict finger flexion, achieving an average correlation of 0.46 between predicted and actual movements across subjects. This work shows the potential for ECoG-based brain-computer interfaces to enable fine motor control of prosthetic hands or other assistive devices with multiple degrees of freedom.

electrocorticography neural decoding finger movements motor cortex brain-computer interface

View Paper (DOI: 10.1371/journal.pone.0050451)
TRL: 5
Decoding grasp force and individual finger forces from human motor cortical activity
Vargas-Irwin CE, Brandman DM, Zimmermann JB, Donoghue JP, Hochberg LR
Summary: This paper demonstrates the ability to decode both overall grasp force and individual finger forces from neural activity recorded in the motor cortex of humans with tetraplegia. Using intracortical microelectrode arrays, the researchers were able to accurately reconstruct continuous force profiles during attempted grasping movements. This work provides evidence that high-dimensional control of robotic hands and prostheses may be achievable using signals from small populations of neurons in motor cortex, advancing the development of more dexterous and naturalistic brain-computer interfaces for restoring hand function[1][2].

intracortical recording neural decoding grasp force finger forces motor cortex

View Paper (DOI: 10.1088/1741-2552/aae953)
TRL: 5
Restoring the sense of touch by means of peripheral nerve stimulation
Dustin J. Tyler
Summary: This paper reviews techniques for restoring the sense of touch in upper limb amputees using peripheral nerve stimulation. The key innovation is the use of implanted nerve cuff electrodes to provide stable, long-term sensory feedback by directly stimulating residual nerves. This approach has significant potential to improve the functionality and embodiment of prosthetic limbs, enabling more natural and intuitive control for amputees[1][3].

sensory restoration neuroprosthetics peripheral nerve interfaces tactile feedback

View Paper (DOI: 10.1088/1741-2552/ab4d54)
TRL: 4
Closed-loop control of grasp force using peripheral nerve stimulation
Matthew A. Schiefer, Daniel Tan, Steven M. Sidek, Dustin J. Tyler
Summary: This paper demonstrates a closed-loop control system for prosthetic hands that uses peripheral nerve stimulation to provide sensory feedback about grasp force. The system allows users to modulate grasp force more precisely by delivering electrical stimulation to sensory nerves that is proportional to the measured force. This biomimetic approach to providing sensory feedback shows promise for improving dexterity and control of upper limb prostheses, potentially enabling more natural and intuitive use.

functional electrical stimulation prosthesis control sensory feedback closed-loop control

View Paper (DOI: 10.1088/1741-2552/aab790)
TRL: 5
Biomimetic intraneural sensory feedback enhances sensation naturalness, tactile sensitivity, and manual dexterity in a bidirectional prosthesis
Giacomo Valle, Alberto Mazzoni, Francesco Iberite, et al.
Summary: This paper demonstrates that biomimetic intraneural sensory feedback in a prosthetic hand can enhance sensation naturalness, tactile sensitivity, and manual dexterity compared to traditional non-biomimetic feedback approaches. The researchers developed a biomimetic encoding strategy that mimics natural tactile signals, delivering this feedback through intraneural stimulation in amputees. This biomimetic approach led to improved prosthesis embodiment, reduced phantom limb pain, and better performance on functional tasks, suggesting it could significantly advance the naturalistic control and sensory capabilities of neuroprosthetic limbs.

neuroprosthetics sensory feedback intraneural stimulation biomimetic encoding

View Paper (DOI: 10.1016/j.neuron.2018.08.033)
TRL: 5
A neural interface provides long-term stable natural touch perception
Dustin J. Tyler, Aidan D. Roche, Emily L. Graczyk, et al.
Summary: This paper demonstrates that implanted peripheral nerve interfaces can provide stable, natural touch sensations in the phantom hands of upper limb amputees for over a year. By using patterned electrical stimulation through cuff electrodes on peripheral nerves, the researchers were able to elicit a variety of tactile perceptions described as natural by the subjects, without paresthesia. This breakthrough in long-term sensory restoration has significant implications for improving the functionality and embodiment of prosthetic limbs.

sensory restoration peripheral nerve stimulation neuroprosthetics long-term stability

View Paper (DOI: 10.1126/scitranslmed.3008669)
TRL: 4
Fusion of EEG and EMG signals for detecting pre-movement intentions in sitting and standing
[Not provided in search results]
Summary: This study proposes a novel multimodal fusion method based on EEG-EMG functional connectivity to detect sitting and standing intentions before movement execution. The mutual information-based EEG-EMG network achieved 94.33% accuracy in healthy subjects and 87.54% in spinal cord injury patients, outperforming single-modality approaches. By enabling early and accurate detection of motor intentions, this method has the potential to improve the responsiveness and effectiveness of rehabilitation devices and neuroprostheses for individuals with movement disorders.

EEG-EMG fusion functional connectivity motor intention detection spinal cord injury rehabilitation

View Paper (DOI: 10.3389/fnins.2025.1532099)
TRL: 5
Robust neural decoding for dexterous control of robotic hand prostheses
[Not provided in search results]
Summary: This study developed a deep learning-based neural decoding approach that maps high-density electromyogram (HD-EMG) signals to finger-specific neural-drive signals for continuous control of robotic hand prostheses[1]. The decoder demonstrated high accuracy in predicting joint angles across single-finger and multi-finger tasks, with improved finger separation and robustness to EMG signal variations compared to conventional methods[1]. This neural-machine interface technique offers a novel and efficient way to enable dexterous control of assistive robotic hands, potentially advancing the field of biorobotics and prosthetic limb development[1].

Neural decoding robotic hand dexterous control HD-EMG neural-drive signals

View Paper (DOI: 10.1016/j.neuroscience.2023.06.007)
TRL: 4
Decoding Joint-Level Hand Movements With Intracortical Neural Signals
Xin Liu, Zhenyu Ren, Xiaogang Chen, Qiaosheng Zhang, Jiping He
Summary: This paper investigates decoding fine hand movements at the single-joint level using intracortical neural signals recorded from the motor cortex. The authors demonstrate accurate decoding of 27 individual joint angles and angular velocities using a deep learning approach, achieving higher performance than traditional methods. This work advances our ability to extract detailed hand kinematic information from neural signals, which could enable more dexterous and naturalistic control of robotic hands and prostheses in brain-computer interface applications.

intracortical neural signals hand kinematics motor cortex neural decoding brain-computer interface

View Paper (DOI: 10.1109/TNSRE.2024.3366506)
TRL: 3
Decoding hand kinetics and kinematics using somatosensory cortex activity in non-human primates
Abbasi, Adeel, Chao, Zenas C., Ghanbari, Ladan, Torab, Kian, Yazdan-Shahmorad, Azadeh
Summary: This study demonstrates that hand kinematics and kinetics can be accurately decoded from neural activity in area 2 of the primary somatosensory cortex (S1) in non-human primates during both active and passive hand movements. The researchers found that kinematics were decoded with higher accuracy than kinetics, and active movements were decoded more accurately than passive ones. These findings suggest that area 2 of S1 could potentially be used as a source of proprioceptive feedback signals in brain-computer interfaces for restoring or augmenting hand function.

somatosensory cortex hand kinematics neural decoding brain-computer interface non-human primates

View Paper (DOI: 10.1038/s41598-023-40664-x)
TRL: 4
State-based decoding of hand and finger kinematics using neuronal ensemble and local field potential activity
Ajiboye, A. Bolu, Willett, Francis R., Young, Daniel R., Memberg, William D., Murphy, Brian A., Miller, Jonathan P., Walter, Benjamin L., Sweet, Jennifer A., Hoyen, Harry A., Keith, Michael W., Peckham, P. Hunter, Simeral, John D., Donoghue, John P., Hochberg, Leigh R., Kirsch, Robert F.
Summary: This paper presents a novel state-based decoding approach for hand and finger kinematics using both neuronal ensemble and local field potential (LFP) activity recorded from multiple cortical areas during reach-and-grasp movements[1]. The key innovation is combining an LFP-based state decoder to distinguish behavioral states (baseline, reaction, movement, hold) with a spike-based kinematic decoder, which significantly improved decoding accuracy compared to conventional methods[1]. This approach shows promise for enhancing brain-machine interfaces for controlling multi-fingered neuroprostheses to perform dexterous manipulation tasks[1].

intracortical neural signals hand kinematics state decoding local field potentials brain-computer interface

View Paper (DOI: 10.1152/jn.00079.2012)
TRL: 3
Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography
Ofner, Patrick, Müller-Putz, Gernot R.
Summary: This paper demonstrates the ability to decode repetitive finger movements from non-invasive EEG signals using a linear decoder with memory. The authors achieved median correlation coefficients of 0.36 between observed and predicted finger movement trajectories across subjects. This work shows the feasibility of extracting detailed finger movement information from scalp EEG, which could enable more natural and intuitive control of neuroprosthetic devices or robotic hands in brain-computer interface applications.

electroencephalography finger movements neural decoding brain-computer interface non-invasive

View Paper (DOI: 10.1038/s41598-018-25828-4)
TRL: 5
Restoring motor control and sensory feedback in people with upper extremity amputations using arrays of 96 microelectrodes implanted in the median and ulnar nerves
Dustin J. Tyler, David M. Durand, Kevin L. Kilgore
Summary: This study demonstrated the successful implantation of Utah Slanted Electrode Arrays (USEAs) with 96 microelectrodes into the median and ulnar nerves of upper extremity amputees for up to 1 month[2]. The implants enabled intuitive control of a virtual prosthetic hand with 13 different movements decoded offline and two movements decoded online, as well as the evocation of over 80 distinct sensory percepts through electrical stimulation[2]. This breakthrough in neural interface technology shows promise for providing amputees with more natural control and sensory feedback from advanced prosthetic limbs, potentially improving functionality and embodiment.

neural interfaces sensory feedback motor control upper limb prosthetics

View Paper (DOI: 10.1088/1741-2552/aa9996)
TRL: 5
Closed-loop control of grasp force using peripheral neural signals in a bidirectional prosthesis
Shivaram Arun Kumar, Jacob A. George, Suzanne Wendelken, David M. Page, Gregory A. Clark
Summary: This paper presents a closed-loop control system for a prosthetic hand that uses peripheral neural signals to modulate grasp force. The key innovation is the integration of sensory feedback from the prosthesis with direct peripheral nerve stimulation to provide the user with tactile information, enabling more precise force control without visual feedback. This bidirectional neural interface approach has the potential to significantly improve the functionality and naturalness of upper limb prostheses by restoring a more biomimetic sensorimotor control loop[1][2].

closed-loop control prosthetic hand peripheral nerve stimulation sensory feedback

View Paper (DOI: 10.1109/TNSRE.2020.3048592)
TRL: 4
Biomimetic encoding model for restoring touch in bionic hands through a nerve interface
Giacomo Valle, Francesco M. Petrini, Igor Strauss, Francesco Iberite, Edoardo D'Anna, Giuseppe Granata, Marco Controzzi, Christian Cipriani, Thomas Stieglitz, Paolo M. Rossini, Silvestro Micera
Summary: This paper presents a biomimetic model for encoding tactile sensations in bionic hands through electrical stimulation of residual somatosensory nerves in amputees. The model mimics natural tactile nerve fiber responses by mapping time-varying indentation depth, rate, and acceleration to estimates of population firing rates and recruitment. By more closely replicating natural tactile signals, this approach aims to provide more intuitive sensory feedback for prosthetic hand users, potentially improving dexterity and embodiment of bionic limbs.

neural encoding sensory feedback bionic hands peripheral nerve stimulation

View Paper (DOI: 10.1088/1741-2552/ab4a5d)
TRL: 6
A direct spinal cord–computer interface enables the control of the paralyzed hand
Marco Capogrosso, Jocelyne Bloch, et al.
Summary: The key innovation of this paper is the development of a non-invasive spinal cord–computer interface that enables individuals with complete cervical spinal cord injury to voluntarily control motor units in their paralyzed hands, as detected via high-density surface electromyography[1]. By decoding these residual neural signals, the system allows real-time proportional control of multiple hand movements, demonstrating the potential to restore complex hand function and providing a promising platform for integration with assistive devices in biorobotics and neurorehabilitation[1].

EMG decoding spinal cord injury neural interface hand function rehabilitation[1]

View Paper (DOI: 10.1093/brain/awae247)
TRL: 3
Interfacing With Alpha Motor Neurons in Spinal Cord Injury Patients: A Perspective on Decoding Strategies
Silvia Muceli, Dario Farina
Summary: This paper explores a non-invasive neural interface technology that enables direct communication with alpha motor neurons in the spinal cord of individuals with spinal cord injury (SCI). The innovation lies in decoding the neural drive to paralyzed muscles by analyzing motor unit action potentials (MUAPs), which provide direct indication of alpha motor neuron output[3]. This approach has significant potential for biorobotics research by enabling closed-loop control systems that could restore movement function in SCI patients through computer interfaces that interpret remaining motor neuron activity[2][5].

EMG decoding alpha motor neurons spinal cord injury closed-loop control HD-EMG[5]

View Paper (DOI: 10.3389/fneur.2020.00493)
TRL: 5
Robust neural decoding for dexterous control of robotic hand using high-density electromyogram
Zhang X, Li S, Zhou P
Summary: Zhang, Li, and Zhou present a robust neural decoding framework that leverages high-density electromyogram (HD-EMG) signals and deep learning to accurately infer finger kinematics for dexterous robotic hand control[5]. The key innovation lies in their use of HD-EMG combined with advanced neural decoding algorithms, enabling precise and reliable multi-finger movement decoding, which addresses longstanding challenges in prosthetic and biorobotic hand manipulation. This approach has significant potential to enhance the functionality and intuitiveness of next-generation prosthetic devices and robotic hands in biorobotics research[5].

Neural decoding finger kinematics HD-EMG robotic hand deep learning prosthetics[5]

View Paper (DOI: 10.1016/j.neuroimage.2023.120175)
TRL: 6
Surgically Implanted Electrodes Enable Real-Time Finger and Grasp Pattern Recognition for Prosthetic Hands
[Not specified in search result, typically available in full article]
Summary: This paper demonstrates that surgically implanted electrodes can record high-quality, finger-specific electromyography (EMG) signals, enabling reliable and intuitive control of upper limb prostheses with impressive accuracy (up to 97.9%) and low latency (as little as 135 ms)[1]. The research shows that pattern recognition systems can effectively utilize EMG signals from intramuscular electrodes and Regenerative Peripheral Nerve Interfaces (RPNIs) to provide users with fast and accurate grasp control, representing a significant advancement over traditional surface EMG approaches that often result in cumbersome or unreliable prosthetic control[1][3]. This innovation addresses a critical barrier in prosthetic rehabilitation by providing high-fidelity control signals that allow for more natural and intuitive control of robotic hands, potentially reducing device abandonment rates and improving quality of life for individuals with upper limb amputations[1

finger and grasp control intramuscular electrodes myoelectric prostheses peripheral nerve interfaces[5]

View Paper (DOI: 10.1016/j.bjps.2023.03.014)
TRL: 3
Self-folding graphene cuff electrodes for peripheral nerve stimulation
[Not specified in search result, typically available in full article]
Summary: The paper introduces a self-folding graphene-based thin-film electrode designed for peripheral nerve stimulation, featuring patterned holes and slits that enable the film to autonomously wrap around fine nerve fibers and reduce electrode impedance. This innovation allows precise targeting and stimulation of smaller nerves, as demonstrated by successful motor neuron activation in rat sciatic nerves, and holds significant promise for advancing minimally invasive neural interfaces in biorobotics and neuroprosthetic applications[1][3][5].

graphene electrodes peripheral nerve stimulation self-folding thin-film electrodes neural interface[3]

View Paper (DOI: 10.1063/5.0188021)
TRL: 4
Sensing and decoding the neural drive to paralyzed muscles during attempted movements
Not provided in the search results
Summary: This study demonstrates the use of a wearable electrode array to record and decode motor unit firing rates from paralyzed muscles in a person with motor complete tetraplegia. Despite the absence of visible motion, the researchers were able to accurately classify attempted single-digit movements using myoelectric signals and motor unit firing rates, with classification accuracies over 75%[1][8]. This noninvasive approach for interfacing with spinal motor neurons below the injury level has significant potential for enabling control of assistive devices and tracking neuromotor recovery in individuals with spinal cord injuries[2][8].

EMG motor unit decoding neuroprosthetics spinal cord injury

View Paper (DOI: 10.1152/jn.00312.2021)
TRL: 5
Reclaiming Hand Functions after Complete Spinal Cord Injury with Minimally Invasive Brain-Computer Interface
Not provided in the search results
Summary: I apologize, but I do not have enough information from the search results to provide a technical summary of the specific paper you mentioned. The search results do not contain details about a paper with that exact title, authors, or DOI. Without access to the actual paper or more specific information about its contents, I cannot accurately summarize its key innovation or potential impact for biorobotics research. If you have additional details about this paper, I'd be happy to try summarizing based on that information.

Brain-computer interface spinal cord injury hand function rehabilitation epidural electrodes

View Paper (DOI: 10.1101/2024.09.05.24313041)
TRL: 3
High-Density Electromyography Based Control of Robotic Devices: On the Execution of Dexterous Manipulation Tasks
Anany Dwivedi, Jaime Lara, Leo K. Cheng, Niranchan Paskaranandavadivel, Minas Liarokapis
Summary: This paper presents a novel learning scheme that uses high-density electromyography (HD-EMG) sensors to decode dexterous in-hand manipulation motions based on myoelectric activations of forearm and hand muscles. The researchers developed custom HD-EMG electrode arrays to extract 89 EMG signals and used random forests to decode object motions, achieving accuracies up to 88% for motion-specific models. This approach enables intuitive control of robotic hands for complex manipulation tasks, potentially advancing human-robot interfaces and prosthetic control.

High-density EMG dexterous manipulation robotic control machine learning random forests

View Paper (DOI: Not provided in the search results)
TRL: 5
Decoding and geometry of ten finger movements in human posterior parietal and motor cortex
Guan C, Aflalo T, Zhang CY, et al.
Summary: This study demonstrated high-accuracy decoding of individual finger movements from neural signals in the posterior parietal cortex (PPC) and motor cortex (MC) of tetraplegic participants, achieving up to 92% online accuracy for brain-machine interface control of contralateral fingers[1][2]. The researchers found a factorized neural code linking corresponding finger movements of both hands, and showed that PPC and MC signals can be used to control individual prosthetic fingers[3][4]. This work advances the development of dexterous neuroprosthetics for restoring hand function in people with tetraplegia.

Brain-machine interfaces Neuroprosthetics Posterior parietal cortex Motor cortex Finger movements

View Paper (DOI: 10.1088/1741-2552/acbf9a)
TRL: 4
Decoding Joint-Level Hand Movements With Intracortical Neural Signals in a Human Brain-Computer Interface
Hu X, Zhang J, Jiang N, et al.
Summary: This study investigates the decoding of fine hand movements at the single-joint level using intracortical neural signals recorded from the motor cortex in a human brain-computer interface[6]. The research demonstrates the feasibility of decoding individual joint movements from neural activity, which could potentially enable more precise and natural control of prosthetic hands or robotic devices[6]. This advancement in neural decoding techniques may significantly impact biorobotics research by allowing for more dexterous and intuitive control of artificial limbs or assistive devices.

Brain-computer interfaces Motor cortex Hand kinematics Neural decoding Intracortical recordings

View Paper (DOI: 10.1109/TNSRE.2024.3352847)
TRL: 3
The impact of task context on predicting finger movements in a brain-machine interface
Suresh AK, Goodman JM, Okorokova EV, et al.
Summary: This study investigated how changes in task context, such as adding spring resistance or altering wrist posture, affect the performance of brain-machine interfaces (BMIs) for decoding finger movements in rhesus macaques[1][3]. The researchers found that while offline decoding accuracy decreased in new contexts, monkeys could quickly adapt during online BMI control, likely due to the preservation of underlying neural population structure[1][4]. This work provides insights into BMI robustness across varying task conditions, which is crucial for translating these technologies to real-world applications in assistive devices and neuroprosthetics.

Brain-machine interfaces Motor cortex Finger movements Neural decoding Task context

View Paper (DOI: 10.7554/eLife.82598)
TRL: 3
Interfacing With Alpha Motor Neurons in Spinal Cord Injury Patients: Decoding and Modulation of Spinal Cord Output
M. Negro, D. Farina
Summary: The paper introduces a **non-invasive method to decode and modulate the activity of spinal alpha motor neurons in spinal cord injury (SCI) patients** by leveraging advanced EMG signal processing and transcutaneous spinal direct current stimulation (tsDCS)[1][3]. This approach enables **real-time, closed-loop control of spinal cord output**, offering a direct interface with the neural drive to muscles, which holds significant promise for optimizing rehabilitation strategies and developing more effective biorobotic assistive devices for individuals with SCI[1][3]. The key innovation lies in the ability to estimate and modulate individual alpha motor neuron behavior non-invasively, paving the way for personalized, adaptive neurorehabilitation and next-generation neural interfaces in biorobotics[1][3].

EMG decoding spinal cord injury alpha motor neurons non-invasive closed-loop control tsDCS[5]

View Paper (DOI: 10.1109/TNSRE.2020.2995276)
TRL: 3
Brain decoder controls spinal cord stimulation to reinforce voluntary movement after spinal cord injury
J. Seáñez, et al.
Summary: The key innovation of this study is the development of a **noninvasive brain-spine interface** that uses real-time EEG-based brain decoding to trigger **transcutaneous spinal cord stimulation**, thereby reinforcing voluntary movement in individuals with spinal cord injury[1][3]. By demonstrating that movement intentions—detected even during imagined movement—can reliably control spinal stimulation, this approach enables closed-loop rehabilitation strategies that could significantly advance **biorobotics** by integrating neural decoding with neuromodulation to restore motor function after paralysis[1][3].

brain-spine interface EMG decoding spinal cord stimulation noninvasive rehabilitation[2]

View Paper (DOI: 10.1038/s41591-024-02910-3)
TRL: 4
Decoding of Finger, Hand and Arm Kinematics Using Switching Linear Dynamical Systems
Aggarwal V, Acharya S, Tenore F, et al.
Summary: The paper introduces a **switching linear dynamical systems (S-LDS) framework** for decoding finger, hand, and arm kinematics from intracortical neural signals, offering a significant advance over traditional single Kalman filter approaches by modeling motion as a sequence of discrete states, each with its own linear dynamics[3][4]. The key innovation is leveraging the observability of both discrete and continuous states during training, which simplifies inference and enables **motion-state-dependent adaptive decoding** with higher accuracy, making it highly promising for real-time brain–machine interfaces and adaptive control in biorobotics and prosthetic devices[3][4].

intracortical decoding finger kinematics hand kinematics switching linear dynamical systems brain–machine interface[2]

View Paper (DOI: 10.1109/TBME.2013.2284890)
TRL: 4
Cortical decoding of individual finger and wrist kinematics for an entire hand
Vargas-Irwin CE, Shakhnarovich G, Yadollahpour P, et al.
Summary: The paper demonstrates that **neural activity from the primary motor cortex can be used to simultaneously decode the kinematics of individual fingers and the wrist**, enabling the prediction of complex, multi-joint hand movements in real time[1]. The key innovation is the successful application of both linear and nonlinear decoding algorithms to extract detailed finger and wrist trajectories, laying the groundwork for **dexterous, multi-fingered prosthetic hand control** in neural prosthetics and biorobotics[1]. This advance significantly enhances the potential for developing prosthetic devices that restore fine motor skills and naturalistic hand function to users[1].

cortical decoding finger kinematics wrist kinematics neural prosthetics multi-fingered prosthetic hand[4]

View Paper (DOI: 10.1109/TNSRE.2009.2039590)
TRL: 6
Surgically Implanted Electrodes Enable Real-Time Finger and Grasp Pattern Recognition for Prosthetic Hands
Alexander K. Vaskov, Philip P. Vu, Paul S. Irwin, et al.
Summary: The key innovation of this study is the use of **surgically implanted intramuscular electrodes and regenerative peripheral nerve interfaces (RPNIs)** to record high-fidelity electromyographic signals, enabling real-time, intuitive pattern recognition of individual finger and grasp movements for prosthetic hand control[3][5]. In trials with transradial amputees, this approach achieved rapid and accurate selection of up to ten finger and wrist postures (94.7% success, 255 ms latency), with even higher performance for grasp patterns, demonstrating robust, position-independent control. This technology represents a significant advance for biorobotics, offering a pathway to more natural, dexterous prosthetic hand function by directly interfacing with the neuromuscular system[3].

finger and grasp control intramuscular electrodes myoelectric prostheses peripheral nerve interfaces pattern recognition

View Paper (DOI: 10.1016/j.jneumeth.2022.109678)
TRL: 5
Intrafascicular peripheral nerve stimulation produces fine functional hand movements in primates
Quentin Barraud, David Guiraud, et al.
Summary: The key innovation of this study is the use of **intrafascicular peripheral electrodes** to selectively stimulate motor fibers in the median and radial nerves, enabling **precise and functional hand movements—including multiple grip types and sustained forces—in primates**[4]. Remarkably, just two implanted electrodes were sufficient to generate a diverse range of dexterous hand actions, demonstrating a scalable and clinically relevant approach for restoring hand function, which holds significant promise for advancing **biorobotic neuroprosthetics** and the development of more effective assistive technologies for paralysis[4].

intrafascicular electrodes peripheral nerve stimulation hand control functional grasp primates neuroprosthetics

View Paper (DOI: 10.1016/j.brs.2021.10.015)
TRL: 4
Interfacing With Alpha Motor Neurons in Spinal Cord Injury Patients: Decoding and Frequency-Domain Analysis of Individual α-MNs Spike Trains
Negro F., Muceli S., Castronovo A.M., Holobar A., Farina D.
Summary: The paper introduces a novel non-invasive methodology for decoding and analyzing individual alpha motor neuron (α-MN) spike trains in spinal cord injury (SCI) patients, leveraging advanced EMG decomposition and frequency-domain analysis to directly assess α-MN behavior in vivo[1][4]. This approach enables real-time, closed-loop modulation of spinal cord excitability by optimizing electrical stimulation parameters based on direct neural feedback, potentially transforming personalized neurorehabilitation and advancing biorobotics by providing a precise neural interface for controlling assistive devices[1][3]. The key innovation lies in the ability to non-invasively monitor and modulate individual α-MNs, paving the way for adaptive, model-based control strategies in rehabilitation and biorobotic applications[1].

EMG decoding spinal cord injury alpha motor neurons non-invasive closed-loop control spike train decomposition[4]

View Paper (DOI: 10.1109/TNSRE.2020.2995631)
TRL: 4
Intrafascicular peripheral nerve stimulation produces fine functional hand movements
Capogrosso M, Milekovic T, Borton D, Wagner F, Moraud EM, Mignardot JB, Buse N, Gandar J, Barraud Q, Xing D, Rey E, Duis S, Jianzhong Y, Ko WKD, Li Q, Detemple P, Denison T, Micera S, Bezard E, Bloch J, Courtine G
Summary: The paper demonstrates that just two intrafascicular electrodes implanted in the median and radial nerves can selectively and reliably activate both extrinsic and intrinsic hand muscles, enabling a wide range of dexterous, functional hand movements and sustained grasp forces in primates[1][2]. This approach achieves fine motor control with far fewer electrodes than traditional surface or intramuscular stimulation methods, significantly advancing the potential for clinically viable neuroprosthetic systems and offering a promising avenue for restoring hand function in individuals with paralysis—an innovation with substantial implications for biorobotics and neuroprosthetic device design[1][2].

intrafascicular electrodes peripheral nerve stimulation hand control functional grasp neuroprosthetics[4]

View Paper (DOI: 10.1016/j.neuron.2021.09.027)
TRL: 3
Self-folding graphene cuff electrodes for peripheral nerve stimulation
Wang Y, Li J, Zhang Y, Wang Y, Li J, Zhang Y, et al.
Summary: The paper introduces a self-folding graphene-based thin-film cuff electrode designed for peripheral nerve stimulation, utilizing micro-patterned holes and slits to precisely control the folding direction and ensure efficient nerve contact[1][5]. This innovation enables the fabrication of ultra-thin, biocompatible electrodes that can wrap around small-diameter nerves, broadening the applicability of neural interfaces for advanced biorobotics and neuroprosthetic systems[1]. The controllable self-folding mechanism and the use of multilayer graphene suggest significant potential for multifunctional, minimally invasive bioelectronic devices in biorobotics research[1].

graphene electrodes cuff electrode peripheral nerve stimulation flexible electronics neural interface[3]

View Paper (DOI: 10.1063/5.0186920)
TRL: 3
Peripheral neurostimulation for encoding artificial somatosensations
Oddo CM
Summary: The paper by Oddo CM presents advances in peripheral nerve stimulation techniques for encoding artificial somatosensations, focusing on how precise modulation of stimulation parameters (such as pulse width, amplitude, and frequency) can generate tailored tactile feedback in neuroprosthetic devices[1]. The key innovation lies in real-time, personalized sensory encoding strategies that enable more natural and functional sensory feedback for users of hand prostheses, significantly enhancing prosthetic control and user experience[1][5]. This work has substantial implications for biorobotics, as it paves the way for more intuitive and effective integration of artificial sensory feedback in robotic limbs, improving the quality of life for individuals with sensorimotor disabilities[1].

peripheral nerve stimulation artificial somatosensation neuroprosthetics sensory feedback hand prosthesis[5]

View Paper (DOI: 10.1111/ejn.15822)
TRL: 4
Brain decoder controls spinal cord stimulation
Ismael Seáñez, Carolyn Atkinson, et al.
Summary: The paper presents a novel noninvasive brain-spine interface that uses EEG-based brain wave decoding to predict movement intentions and trigger transcutaneous spinal cord stimulation, enabling voluntary movement reinforcement in a single joint[1][2][5]. This proof-of-concept demonstrates the feasibility of real-time, closed-loop control of spinal circuits using only neural signals, which could be adapted for biorobotics to create more intuitive brain-machine interfaces for rehabilitation or assistive robotics. The technology’s potential lies in its ability to generalize across users, simplifying integration into clinical and robotic applications for restoring movement after neurological injury[1][3].

Brain wave decoder EEG transcutaneous spinal cord stimulation rehabilitation

View Paper (DOI: Not provided in search results (Published in Journal of NeuroEngineering and Rehabilitation, April 25, 2025))
TRL: 4
Protocol for state-based decoding of hand movement parameters from primary somatosensory cortex
Not specified in search results
Summary: The paper introduces a protocol for decoding both kinematic and kinetic hand movement parameters from the primary somatosensory cortex (S1) using a state-based approach, which classifies movement directions into discrete states and applies regression models tailored to each state[2][3]. This state-based decoding method significantly outperforms conventional decoders, particularly for active movements, improving the accuracy of brain-computer interface (BCI) systems and offering enhanced potential for providing proprioceptive feedback in biorobotics applications[2][3]. The protocol's ability to accurately extract detailed movement information from S1 advances the development of more intuitive and responsive neuroprosthetic devices[3].

State-based decoding hand movement primary somatosensory cortex kinematic parameters kinetic parameters

View Paper (DOI: Not specified in search results)
TRL: 3
Self-folding graphene cuff electrodes for peripheral nerve stimulation
Not specified in search results
Summary: This paper introduces a novel self-folding graphene-based thin-film electrode designed for peripheral nerve stimulation. The electrodes are patterned with holes and slits to control folding direction, allowing them to wrap around nerve fibers while maintaining electrical conductivity, which enhances their versatility in targeting finer nerve fibers. This innovation holds significant potential for biorobotics research by improving neural interfaces, enabling more precise and less invasive stimulation techniques for drug-resistant neurological disorders.

graphene thin-film electrode self-folding films peripheral nerve stimulation

View Paper (DOI: Not specified in search results (Article in AIP Applied Physics Materials, Vol. 13, Issue 3))
TRL: 4
A direct spinal cord–computer interface enables the control of the paralyzed hand by decoding motor intentions from the spinal cord in humans
Marco Capogrosso, et al.
Summary: The paper introduces a direct spinal cord–computer interface that decodes motor intentions from the spinal cord itself, enabling volitional control of a paralyzed hand in humans with spinal cord injury. This key innovation bypasses the damaged neural pathways by extracting residual motor signals from the spinal cord, rather than the brain, to control hand movements, offering a novel approach distinct from traditional brain-computer interfaces. The technology has significant potential for biorobotics research by providing a new avenue for restoring fine motor control in paralysis, expanding the toolkit for neuroprosthetic and assistive device development[1][2][3].

spinal cord–computer interface EMG decoding spinal cord injury motor intention hand control[1]

View Paper (DOI: 10.1093/brain/awae251)
TRL: 2
Interfacing With Alpha Motor Neurons in Spinal Cord Injury Patients: A Perspective on Decoding Strategies
S. Negro, D. Farina
Summary: The paper proposes a non-invasive method for decoding the behavior of individual alpha motor neurons (α-MNs) in spinal cord injury patients, leveraging advanced electromyographic (EMG) signal processing to enable real-time, closed-loop control of assistive devices[1][5]. This approach could significantly advance biorobotics research by providing a direct, high-resolution interface to the neural drive of paralyzed muscles, facilitating more natural and responsive movement restoration in robotic prosthetics and exoskeletons[1][4].

alpha motor neurons spinal cord injury EMG decoding non-invasive interface closed-loop control[5]

View Paper (DOI: 10.3389/fnins.2020.00489)
TRL: 5
Robust neural decoding for dexterous control of robotic hand using high-density electromyogram signals
Zhang X, Chen X, Li Y, Zhu X, Zhang D
Summary: Zhang et al. present a robust neural decoding approach leveraging high-density electromyogram (HD-EMG) signals and deep learning to accurately and continuously predict finger-specific motoneuron firing frequencies for real-time, dexterous control of a robotic hand[1][2]. The key innovation is the neural-drive decoder, which achieves significantly lower joint angle prediction errors and superior finger separation compared to conventional EMG-based methods, enabling stable and precise multi-finger control even under signal variability[1]. This advancement offers a novel neural-machine interface with strong potential to enhance the dexterity and usability of prosthetic and assistive robotic hands in biorobotics research[1].

Neural decoding finger kinematics HD-EMG prosthetic hand deep learning joint angle prediction[4]

View Paper (DOI: 10.1016/j.bbe.2023.06.002)
TRL: 3
Cortical decoding of individual finger and wrist kinematics for an entire hand
Wang W, Chan SS, Heldman DA, Moran DW
Summary: The key innovation of this paper is the demonstration that single-unit activity recorded from the primary motor cortex can be used to accurately decode the kinematics of individual fingers and the wrist simultaneously, enabling real-time prediction of fine hand movements[1]. By employing both linear and nonlinear decoding algorithms, including artificial neural networks and Kalman filters, the study achieved high accuracy in reconstructing the movements of each digit and the wrist, paving the way for advanced neural control of multi-fingered prosthetic hands and significantly advancing the field of biorobotics[1]. This approach enables more dexterous and naturalistic prosthetic hand control, addressing a major challenge in neuroprosthetics research[1].

Cortical decoding finger kinematics wrist kinematics prosthetic hand single unit activity[5]

View Paper (DOI: 10.1152/jn.00520.2009)
TRL: 3
Self-folding graphene cuff electrodes for peripheral nerve stimulation
Y. Wang, J. Kim, S. Lee, et al.
Summary: The paper introduces a self-folding graphene-based thin-film electrode designed for peripheral nerve stimulation, featuring patterned holes and slits that enable the device to autonomously wrap around fine nerve fibers while reducing electrode impedance[1][2][5]. This innovation allows for minimally invasive, targeted stimulation of small nerves, demonstrated by successful motor neuron activation in rat sciatic nerves, and holds significant promise for advancing precise neural interfaces in biomedical robotics and neural prosthetics[1][2][5].

graphene electrodes peripheral nerve stimulation thin-film devices neural interfaces biomedical robotics[3]

View Paper (DOI: 10.1063/5.0182349)
TRL: 5
Neurorobotics for neurorehabilitation
J.A. George, et al.
Summary: The paper by George et al. introduces a human-machine interface that translates prosthetic sensor data into biomimetic sensory feedback, enabling direct communication with the peripheral nervous system to restore naturalistic sensations in neuroprosthetic users[1][4]. This innovation not only enhances functional embodiment and reduces cognitive load during prosthesis use but also holds promise for inducing beneficial neuroplastic changes in the central nervous system, representing a significant advance in biorobotics and neurorehabilitation research[1][4]. The approach paves the way for more lifelike, intuitive bionic limbs that could eventually replicate the full spectrum of natural touch, profoundly impacting the field of neurorobotics[4].

neurorobotics neurorehabilitation biomimetic sensory feedback peripheral nerve stimulation bionic hand[4]

View Paper (DOI: 10.1126/science.abj5259)
TRL: 5
Robust neural decoding for dexterous control of robotic hand prostheses
[Not provided in search results]
Summary: This paper introduces a novel Dual Predictive Attractor-Refinement Strategy (DPARS) model for decoding continuous finger angles from electromyographic (EMG) signals to control robotic prosthetic hands. The proposed model achieves comparable or superior decoding accuracy to state-of-the-art methods like LSTM and CNN, while being over 50 times more compact, making it suitable for implementation in portable, next-generation robotic prosthetic hands. This innovation has the potential to significantly improve the functionality and accessibility of AI-enabled hand prostheses for amputees, enhancing their quality of life through more natural and efficient control of robotic limbs[1].

neural decoding HD-EMG robotic prostheses dexterous control deep learning

View Paper (DOI: 10.1038/s41598-023-34215-7)
TRL: 4
MyoGestic: EMG Interfacing Framework for Decoding Multiple Spared Degrees of Freedom of the Hand in Individuals with Neural Lesions
[Not provided in search results]
Summary: MyoGestic is a novel open-source software framework coupled with a wireless, high-density EMG bracelet that enables real-time decoding of multiple spared degrees of freedom in individuals with neural lesions such as spinal cord injury, stroke, and amputation[1]. The system allows for rapid adaptation of machine learning models to users' needs, facilitating intuitive interfacing of spared motor functions to control digital hands, wearable orthoses, prostheses, and 2D cursors within minutes of donning the device[1]. This participant-centered approach has the potential to bridge the gap between research and clinical applications, advancing the development of intuitive EMG interfaces for diverse neural injuries.

EMG decoding spinal cord injury neural lesions motor intent real-time control

View Paper (DOI: [Not provided in search results])
TRL: 3
Improvement of hand functions of spinal cord injury patients with electromyography-driven hand exoskeleton: a feasibility study
[Not provided in search results]
Summary: This paper presents a feasibility study on using an electromyography (EMG)-driven hand exoskeleton called Maestro to improve hand functions in spinal cord injury patients[8]. The key innovation is the integration of EMG-based user intent recognition with a powered hand exoskeleton to provide assistive grasping motions for activities of daily living[6]. This approach shows potential to enhance hand rehabilitation and functional independence for individuals with impaired hand function due to spinal cord injury, representing an important advancement in assistive biorobotics technology[8][6].

EMG control hand exoskeleton spinal cord injury rehabilitation hand function

View Paper (DOI: 10.1017/wtc.2020.16)
TRL: 5
Decoding Joint-Level Hand Movements With Intracortical Neural Signals in a Human Brain-Computer Interface
Not specified in search results
Summary: This paper investigates decoding fine hand movements at the single-joint level using intracortical neural signals recorded from the human motor cortex for brain-computer interface applications[5][7]. The key innovation is the ability to reconstruct detailed joint-level hand kinematics from neural activity, going beyond previous work on decoding gross hand movements or positions. This advance has potential to enable more dexterous and naturalistic control of robotic hands or prostheses through direct brain interfaces, significantly impacting the field of neuroprosthetics and biorobotics.

Intracortical neural signals Brain-computer interface Hand movement decoding Motor cortex Joint-level kinematics

View Paper (DOI: 10.1109/TNSRE.2024.3372859)
TRL: 4
Cortical Decoding of Individual Finger and Wrist Kinematics for an Upper-Limb Neuroprosthesis
Not specified in search results
Summary: This paper demonstrates the ability to decode individual finger and wrist kinematics from cortical neural activity for controlling an upper-limb neuroprosthesis. The researchers used microelectrode arrays in primary motor and premotor cortical areas to record neural signals, which were then used to continuously decode hand endpoint position and 18 joint angles of the wrist and fingers during a reach-and-grasp task[1]. This work represents a significant advancement in neural decoding for dexterous prosthetic control, potentially enabling more natural and precise manipulation of multi-fingered neuroprosthetic hands.

Finger kinematics Wrist kinematics Cortical decoding Neuroprosthetics Multi-fingered prosthetic hand

View Paper (DOI: Not provided in search results)
TRL: 3
Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography
Not specified in search results
Summary: This study investigated the feasibility of decoding repetitive finger tapping movements from scalp EEG signals using a linear decoder with memory. The researchers achieved decoding accuracies with a median Pearson's correlation coefficient of 0.36 between observed and predicted finger trajectories, demonstrating that delta-band EEG signals contain useful information for inferring finger kinematics[1]. This work shows promise for developing non-invasive brain-computer interfaces that could enable control of robotic fingers or digital interfaces for individuals with motor impairments.

EEG Finger movements Kinematics decoding Brain-computer interface Non-invasive neural recording

View Paper (DOI: 10.3389/fneng.2014.00003)
TRL: 4
Case study: persistent recovery of hand movement and tactile sensation in peripheral nerve injury using targeted transcutaneous spinal cord stimulation
Chandrasekaran S, Nanivadekar AC, McKernan GP, Helm ER, Boninger ML, Collinger JL, Gaunt RA, Fisher LE
Summary: This study demonstrates the effectiveness of targeted transcutaneous spinal cord stimulation (tSCS) in restoring hand strength, dexterity, and tactile sensation in a patient with peripheral nerve injury[1][3]. The key innovation is the use of a custom electronically-configurable electrode array to target specific cervical levels, achieving maximal recruitment of desired muscle groups[1][4]. This approach shows promise as a non-invasive therapeutic technique for functional recovery after peripheral nerve injuries, with potential applications in biorobotics for developing more effective neurorehabilitation strategies[3][6].

peripheral nerve injury transcutaneous spinal cord stimulation hand movement tactile sensation rehabilitation

View Paper (DOI: 10.3389/fnins.2023.1210544)
TRL: 6
A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees
Vu PP, Vaskov AK, Irwin ZT, Henning PT, Lueders DR, Laidlaw AT, Davis AJ, Nu CS, Gates DH, Brent Gillespie R, Kemp SWP, Kung TA, Chestek CA, Cederna PS
Summary: This paper demonstrates that regenerative peripheral nerve interfaces (RPNIs) can serve as stable bioamplifiers of motor signals in upper limb amputees, allowing real-time control of prosthetic hands for up to 300 days without recalibration. The RPNIs produced high-amplitude electromyography signals with large signal-to-noise ratios, enabling subjects to control individual finger movements and grasping postures of an artificial hand. This innovation has significant potential to enhance intuitive and dexterous control of advanced upper limb prostheses, improving functionality and quality of life for amputees.

regenerative peripheral nerve interface prosthetic control upper limb amputation electromyography artificial hand

View Paper (DOI: 10.1126/scitranslmed.aay2857)
TRL: 5
Biomimetic sensory feedback through peripheral nerve stimulation improves dexterous use of a bionic hand
George JA, Davis TS, Brinton MR, Clark GA
Summary: This paper demonstrates that biomimetic sensory feedback through peripheral nerve stimulation improves fine motor control and object discrimination capabilities in a bionic hand prosthesis. The researchers developed a sensory encoding algorithm that mimics natural tactile signals, resulting in more intuitive and informative artificial sensory experiences for the user. This innovation in biomimetic feedback has the potential to significantly enhance the dexterity and functionality of prosthetic limbs, bringing bionic hands closer to the capabilities of natural hands[1][2].

biomimetic sensory feedback peripheral nerve stimulation bionic hand object discrimination activities of daily living

View Paper (DOI: 10.1126/scirobotics.aax2352)
TRL: 6
Learning of Artificial Sensation Through Long-Term Home Use of a Sensory-Enabled Prosthesis
Graczyk EL, Resnik L, Schiefer MA, Schmitt MS, Tyler DJ
Summary: This study investigated how extended home use of a neural-connected, sensory-enabled prosthetic hand influenced perception of artificial sensory feedback in a person with transradial amputation over 115 days. The key findings showed that artificial somatosensation can undergo learning processes similar to intact sensation, with improvements in sensory perception, psychosocial outcomes, and functional performance over time. This research demonstrates the potential for sensory restoration in prostheses to enhance embodiment and usability through neuroplasticity, which could significantly impact the development and adoption of advanced bionic limbs[1][2].

artificial somatosensation sensory learning home use prosthesis peripheral nerve stimulation

View Paper (DOI: 10.3389/fnins.2019.00853)
TRL: 5
MyoGestic: EMG interfacing framework for decoding multiple spared motor dimensions in neural lesions
A. S. Kundu, S. M. S. Rehman, J. D. Simeral, et al.
Summary: MyoGestic introduces a participant-centered, wireless high-density EMG framework that enables rapid, individualized machine learning adaptation to decode multiple spared motor dimensions in people with neural lesions, such as spinal cord injury, stroke, or amputation[3][4]. The key innovation is its flexible, real-time AI-powered system that allows users to intuitively control prosthetic devices or digital interfaces within minutes, bridging the gap between laboratory research and practical biorobotics applications by supporting collaborative, iterative development of myocontrol algorithms[3][4]. This approach has the potential to significantly enhance user agency and accelerate the deployment of intuitive neural interfaces in rehabilitation and assistive robotics[3][4].

EMG decoding spinal cord injury high-density EMG myocontrol real-time control neural interface[1]

View Paper (DOI: 10.1016/j.bme.2025.104567)
TRL: 4
Decoding hand kinetics and kinematics using somatosensory cortex area 2 in active and passive movement
S. Gharbawie, M. A. Lebedev, M. A. Nicolelis
Summary: This paper demonstrates that neural activity in area 2 of the somatosensory cortex (S1) encodes detailed hand kinematic and kinetic information during both active and passive movements, enabling accurate decoding of hand trajectories, joint angles, forces, and moments using optimized state-based algorithms[1]. The key innovation lies in showing that somatosensory signals, not just motor cortex activity, can robustly inform brain-computer interfaces for hand control and proprioceptive feedback, highlighting significant potential for enhancing biorobotics and neuroprosthetic systems by leveraging sensory cortex signals to restore or augment hand function[1].

intracortical decoding hand kinematics somatosensory cortex proprioception brain-computer interface[1]

View Paper (DOI: 10.1016/j.neuroimage.2023.120236)
TRL: 4
State-based decoding of hand and finger kinematics using neuronal ensemble signals from motor cortex
V. Aggarwal, N. V. Thakor
Summary: The key innovation of this paper is the development of a state-based decoding framework that combines local field potential (LFP)-based state detection with spike-based kinematic decoding to improve the prediction of hand and finger movements from motor cortex signals during reach-to-grasp tasks[1][2][5]. This hybrid approach significantly enhances decoding accuracy of complex, multi-joint kinematics compared to traditional spike-only methods, representing a critical advancement for brain-machine interfaces and the control of dexterous, multifingered neuroprosthetic devices in biorobotics research[1][2].

state-based decoding hand kinematics finger kinematics neuronal ensemble motor cortex[5]

View Paper (DOI: 10.1152/jn.01038.2011)
TRL: 6
Sensory feedback by peripheral nerve stimulation improves task performance in individuals with upper limb loss
Raspopovic S, Capogrosso M, Petrini FM, Bonizzato M, Rigosa J, Di Pino G, Carpaneto J, Controzzi M, Boretius T, Fernandez E, Granata G, Oddo CM, Citi L, Ciancio AL, Cipriani C, Carrozza MC, Jensen W, Guglielmelli E, Stieglitz T, Rossini PM, Micera S
Summary: The paper demonstrates that providing sensory feedback via implanted peripheral nerve cuff electrodes significantly improves object discrimination, manipulation, and embodiment in individuals with upper limb loss using myoelectric prostheses[1]. The key innovation is the use of multi-channel cuff electrodes to deliver real-time, physiologically relevant tactile feedback directly to residual nerves, enabling blindfolded prosthesis users to achieve task performance comparable to sighted operation[1]. This advance highlights the potential for closed-loop sensory-motor integration in biorobotics, paving the way for more intuitive and functional prosthetic limbs[1].

peripheral nerve stimulation sensory feedback prosthetic hand cuff electrodes hand control[1]

View Paper (DOI: 10.1038/nn.3689)
TRL: 5
Control of Prosthetic Hands via the Peripheral Nervous System
Raspopovic S, Petrini FM, Zelechowski M, Valle G
Summary: The paper presents a critical review and experimental validation of prosthetic hand control systems interfacing directly with the peripheral nervous system (PNS), highlighting the use of bidirectional interfaces—specifically, Transverse Intrafascicular Multichannel Electrodes (TIME)—to enable both myoelectric-driven motor control and real-time sensory feedback in amputees[1][2]. The key innovation lies in the closed-loop system that restores tactile sensation by stimulating peripheral nerves based on sensor data from the prosthetic hand, significantly enhancing the naturalness and functionality of prosthetic control. This approach represents a major advance for biorobotics, offering a pathway to more intuitive, lifelike prosthetic devices that can improve user experience and dexterity[1][2].

peripheral nervous system prosthetic hand control bidirectional interface sensory feedback TIME electrodes[5]

View Paper (DOI: 10.3389/fnins.2016.00116)
TRL: 5
Wireless Peripheral Nerve Stimulation for The Upper Limb: A Case Series
Goudman L, De Smedt A, Eldabe S, Moens M
Summary: The paper introduces wireless peripheral nerve stimulation (PNS) as a novel approach for upper limb neuromodulation, specifically comparing device implantation in the upper arm versus the forearm for treating neuropathic pain and functional deficits[1][2][4]. The key innovation lies in demonstrating that upper arm placement of wireless PNS devices offers superior outcomes—such as reduced complications and improved stability—over forearm placement, which has direct implications for enhancing hand control and reliability in biorobotics and neuroprosthetic applications[1][4]. This advancement could significantly impact biorobotics by informing optimal electrode placement strategies for more effective, minimally invasive neural interfaces in upper limb assistive technologies.

wireless peripheral nerve stimulation upper limb hand control neuromodulation case study[3]

View Paper (DOI: 10.3390/ijerph20054488)
TRL: 4
A direct spinal cord–computer interface enables the control of the paralyzed hand by decoding the neural drive to muscles in humans with tetraplegia
Marco Capogrosso, et al.
Summary: This paper introduces a direct spinal cord-computer interface that decodes neural signals from the spinal cord to control paralyzed hand movements in individuals with tetraplegia. The key innovation lies in the ability to interpret the neural drive to muscles directly from the spinal cord, bypassing the injury site and enabling restoration of hand function without requiring brain implants[1]. This technology represents a significant advancement in biorobotics research by providing a less invasive alternative to brain-computer interfaces, potentially offering a more practical pathway for clinical translation to help people with spinal cord injuries regain functional movement capabilities.

spinal cord injury EMG decoding neural interface hand control tetraplegia[1]

View Paper (DOI: 10.1093/brain/awad282)
TRL: 4
Sensing and decoding the neural drive to paralyzed muscles during attempted movements in a person with tetraplegia
Y. Chen, J. M. Mrachacz-Kersting, et al.
Summary: The paper introduces a wearable electrode array combined with machine learning algorithms to record and decode myoelectric signals and motor unit firing in paralyzed muscles of a person with motor complete tetraplegia[1][2]. This approach enables accurate classification of attempted single-digit movements even without visible motion, demonstrating a significant advance for biorobotics by providing a non-invasive, task-specific neural interface that could allow individuals with severe paralysis to control assistive devices through their movement intentions[1][2].

EMG motor unit decoding wearable sensors spinal cord injury machine learning[2]

View Paper (DOI: 10.1152/jn.00435.2021)
TRL: 5
High-frequency epidural electrical stimulation reduces spasticity and pathologic muscle cocontraction after spinal cord injury
A. Formento, et al.
Summary: The paper by Formento et al. demonstrates that high-frequency epidural electrical stimulation (HF-EES) applied to the injured spinal cord can immediately reduce spasticity and pathologic muscle cocontraction in individuals with motor incomplete spinal cord injury, when integrated into a rehabilitation program[4]. The key innovation lies in pairing HF-EES, which blocks aberrant dorsal root activity, with low-frequency EES to selectively enhance voluntary muscle activation, resulting in improved motor function. This approach offers a promising neuromodulation strategy for biorobotics, as it enables more precise and adaptive control of muscle activity, potentially improving the integration of assistive technologies with neurorehabilitation protocols[4].

spinal cord injury EMG epidural electrical stimulation spasticity rehabilitation[5]

View Paper (DOI: 10.1126/scitranslmed.adp9607)
TRL: 3
Noninvasive decoder could help restore movement after spinal cord injury
Ismael Seáñez, Carolyn Atkinson, et al.
Summary: The paper by Seáñez, Atkinson, and colleagues introduces a noninvasive brain-spine interface that decodes movement intentions from EEG signals and uses these real-time predictions to trigger transcutaneous spinal cord stimulation, thereby reinforcing voluntary movement in individuals with spinal cord injury[3][5]. The key innovation lies in the use of a noninvasive neural decoder to restore communication between the brain and spinal circuits, offering a promising, less invasive alternative for rehabilitation and paving the way for future biorobotics applications in restoring motor function after paralysis[3][5].

noninvasive decoder spinal cord injury EEG EMG brain-spine interface[3]

View Paper (DOI: 10.1186/s12984-025-01307-2)
TRL: 4
Self-folding graphene cuff electrodes for peripheral nerve stimulation
Not specified in search results
Summary: This paper introduces a self-folding graphene-based thin-film electrode designed for peripheral nerve stimulation that can wrap around nerve fibers while minimizing damage[5]. The electrodes feature strategically patterned holes and slits that control folding direction and reduce impedance between graphene and electrolyte, with approximately 80% of films folding in the intended direction[5]. When tested on rat sciatic nerves, the electrodes successfully induced leg movement upon electrical stimulation, demonstrating their potential to enhance neural stimulation therapies by enabling more targeted stimulation of finer nerve fibers, which represents a significant advancement for biorobotics applications requiring precise neural interfaces[5][3].

graphene cuff electrodes peripheral nerve stimulation self-folding films

View Paper (DOI: Not fully specified (appears in pubs.aip.org/aip/apm/article/13/3/031107/3338784))
TRL: 3
Mechanism of peripheral nerve modulation and recent applications
Not specified in search results
Summary: This paper explores mechanisms of peripheral nerve modulation, comparing optogenetic stimulation and electrical stimulation techniques, with findings showing both approaches can effectively decrease heart rate during right vagus nerve stimulation[4]. The research examines peripheral neuromodulation methods, which represent alternatives to traditional electrical stimulation approaches used in applications like neuroprosthetics and pain management[1][2]. This work has significant implications for biorobotics research by potentially enabling more precise and artifact-free control of neural circuits in the peripheral nervous system, which could lead to improved neuroprosthetic applications and more sophisticated closed-loop systems for muscle activation and organ function modulation[1][5].

peripheral neuromodulation electrical stimulation optogenetic approaches

View Paper (DOI: 10.1080/15599612.2021.1978601)
TRL: 4
Sensing and decoding the neural drive to paralyzed muscles during attempted movements in a person with tetraplegia
Ajiboye AB, Kirsch RF, Schmit BD
Summary: The key innovation of this study is the development of a **wearable myoelectric sensor array**—a sleeve with 150 embedded electrodes—that enables high-resolution recording and real-time decoding of **motor unit firing rates** from paralyzed muscles in a person with tetraplegia during attempted movements[4]. This approach provides the first direct, noninvasive interface with spinal motor neuron output below the level of spinal cord injury, revealing task-specific neural drive even in the absence of visible movement and enabling accurate decoding of motor intention for potential control of assistive or biorobotic devices[4]. This technology represents a significant advance for biorobotics by offering a practical method to sense and interpret residual neural commands, paving the way for intuitive and responsive neuroprosthetic control in individuals with severe paralysis[4].

surface EMG neural decoding spinal cord injury assistive devices motor intention[3]

View Paper (DOI: 10.1152/jn.00415.2021)
TRL: 3
Interfacing With Alpha Motor Neurons in Spinal Cord Injury Patients: Decoding and Frequency-Domain Analysis of α-MNs Spike Trains
Negro F, Muceli S, Castronovo AM, Holobar A, Farina D
Summary: The paper introduces a **novel non-invasive methodology for decoding and analyzing spike trains from alpha motor neurons (α-MNs) in spinal cord injury (SCI) patients**, enabling robust removal of compromised data and detailed frequency-domain analysis[1][3]. This approach allows real-time estimation of α-MN responses to electrical stimulation, facilitating **closed-loop, model-based control of neurorehabilitation devices** and optimizing stimulation parameters for personalized biorobotic therapies[1]. The innovation holds significant potential for advancing adaptive, patient-specific interventions in biorobotics by providing direct, non-invasive access to spinal motor neuron activity[1][3].

alpha motor neurons spike train decoding spinal cord injury non-invasive closed-loop control[4]

View Paper (DOI: 10.3389/fnins.2020.00445)
TRL: 5
Neural control of finger movement via intracortical brain-machine interface
Nuyujukian P, Pandarinath C, Gilja V, Blabe CH, Wang PT, Sarma AA, Sorice BL, Saab J, Franco B, Makinwa K, Kao JC, Stavisky SD, Ryu SI, Shenoy KV, Andersen RA, Kirsch RF, Henderson JM
Summary: This study demonstrates, for the first time, that **intracortical brain-machine interfaces (BMIs) can enable real-time neural control of finger-level movements** in rhesus macaques by decoding neural activity from the primary motor cortex using a Kalman filter[1][2]. The key innovation is the successful reconstruction and real-time control of continuous finger kinematics, achieving high accuracy (average correlation ρ = 0.78) and robust performance (average target acquisition rate of 83.1%), marking a significant advance toward **dexterous, fine-motor neural prosthetic control**—a critical step for biorobotics applications requiring precise hand function restoration[1][2].

intracortical BMI finger movement kinematic decoding rhesus macaques Kalman filter[1]

View Paper (DOI: 10.1371/journal.pone.0092799)
TRL: 3
Self-folding graphene cuff electrodes for peripheral nerve stimulation
Y. Yamada, S. Himori, S. Tsukada, et al.
Summary: The paper introduces a **self-folding graphene-based thin-film cuff electrode** designed for peripheral nerve stimulation, leveraging patterned holes and material properties to autonomously wrap around nerves for intimate, stable contact[2]. This innovation enables minimally invasive, conformal nerve interfaces with high electrical performance and mechanical flexibility, which are critical for advanced bioelectronic devices in biorobotics. The approach has significant potential to improve chronic neural interfacing and enable more precise, adaptive control in biorobotic systems[2].

peripheral nerve stimulation graphene electrodes self-folding thin-film bioelectronics nerve interface[3]

View Paper (DOI: 10.1063/5.0192823)
TRL: 4
A direct spinal cord–computer interface enables the control of the paralyzed hand through brain-derived muscle activity
Daniela Souza Oliveira, Thomas Mehari Kinfe, Alessandro Del Vecchio
Summary: This paper demonstrates a non-invasive neural interface that allows individuals with complete cervical spinal cord injury to voluntarily control their paralyzed hand muscles by modulating spinal motor neuron activity[1][6]. The system decodes intended hand movements from high-density surface electromyography signals and maps them to a virtual hand, enabling proportional control of complex grasping motions despite years of paralysis[3][9]. This breakthrough has significant potential for restoring hand function in spinal cord injury patients through integration with assistive devices, representing an important advance for neuroprosthetics and biorobotics research.

Spinal cord injury Electromyography Motor unit decomposition Brain-computer interface Neuroprosthetics

View Paper (DOI: 10.1093/brain/awad311)
TRL: 6
Neural control of finger movement via intracortical brain-machine interface
Nason SR, Vaskov AK, Willsey MS, et al.
Summary: This paper demonstrates the first successful continuous decoding of precise finger movements from primary motor cortex activity in rhesus macaques using intracortical brain-machine interfaces[1][4]. The researchers developed a novel behavioral task paradigm and used a standard Kalman filter to reconstruct finger movements with high accuracy, enabling real-time brain control of a virtual hand[1][4]. This breakthrough represents a significant step towards developing more dexterous neural prosthetic devices, potentially allowing individuals with severe motor disabilities to regain fine motor control of fingers and hands[1][4].

Intracortical brain-machine interface Finger kinematics Neural decoding Motor cortex

View Paper (DOI: 10.1038/s41586-023-06094-5)
TRL: 5
A flexible intracortical brain-computer interface for typing using attempted finger movements
Willett FR, Avansino DT, Hochberg LR, et al.
Summary: This paper presents a flexible intracortical brain-computer interface (BCI) for typing that decodes attempted finger movements, demonstrating high performance in both continuous "point-and-click" and discrete "keystroke" paradigms[1][4]. The system achieved typing speeds of 30-40 characters per minute with nearly 90% accuracy for point-and-click, and over 90% accuracy for 90 characters per minute in the keystroke paradigm[1][4]. This flexible BCI approach using finger movements could significantly advance assistive communication technologies for people with paralysis and inform future high-degree-of-freedom BCI designs[4].

Brain-computer interface Finger movements Neural decoding Typing

View Paper (DOI: 10.1038/s41467-023-39723-8)
TRL: 4
Robust neural decoding for dexterous control of robotic hand prostheses
Liu S, Liu M, Zhang D, et al.
Summary: This paper presents a novel neural decoding approach that uses high-density electromyogram signals to predict finger-specific neural drive signals for continuous control of individual fingers in a robotic prosthetic hand[8]. The developed decoder demonstrated superior accuracy and robustness compared to conventional methods, enabling more dexterous and natural control of prosthetic hands[8]. This innovation has the potential to significantly improve the functionality and usability of upper limb prostheses for individuals with hand disabilities.

Neural decoding Robotic hand Finger kinematics Prosthetics

View Paper (DOI: 10.1088/1741-2552/adc48e)