The speech neuroprosthesis

Nature reviews. Neuroscience, Journal Year: 2024, Volume and Issue: 25(7), P. 473 - 492

Published: May 14, 2024

Language: Английский

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A high-performance brain–computer interface for finger decoding and quadcopter game control in an individual with paralysis

Nature Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 20, 2025

Abstract People with paralysis express unmet needs for peer support, leisure activities and sporting activities. Many within the general population rely on social media massively multiplayer video games to address these needs. We developed a high-performance, finger-based brain–computer-interface system allowing continuous control of three independent finger groups, which thumb can be controlled in two dimensions, yielding total four degrees freedom. The was tested human research participant tetraplegia due spinal cord injury over sequential trials requiring fingers reach hold targets, an average acquisition rate 76 targets per minute completion time 1.58 ± 0.06 seconds—comparing favorably prior animal studies despite twofold increase decoded More importantly, positions were then used virtual quadcopter—the number-one restorative priority participant—using brain-to-finger-to-computer interface allow dexterous navigation around fixed- random-ringed obstacle courses. expressed or demonstrated sense enablement, recreation connectedness that addresses many people paralysis.

Language: Английский

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Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations

Journal of Child Neurology, Journal Year: 2023, Volume and Issue: 38(3-4), P. 223 - 238

Published: March 1, 2023

Invasive brain-computer interfaces hold promise to alleviate disabilities in individuals with neurologic injury, fully implantable interface systems expected reach the clinic upcoming decade. Children severe disabilities, like quadriplegic cerebral palsy or cervical spine trauma, could benefit from this technology. However, they have been excluded clinical trials of intracortical date. In manuscript, we discuss ethical considerations related use invasive children disabilities. We first review technical hardware and software for application children. then issues motor pediatric neurosurgery. Finally, based on input a multidisciplinary panel experts fields (functional restorative neurosurgery, mathematics artificial intelligence research, neuroengineering, ethics, pragmatic ethics), formulate initial recommendations regarding

Language: Английский

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Decoding motor plans using a closed-loop ultrasonic brain–machine interface

Nature Neuroscience, Journal Year: 2023, Volume and Issue: 27(1), P. 196 - 207

Published: Nov. 30, 2023

Brain-machine interfaces (BMIs) enable people living with chronic paralysis to control computers, robots and more nothing but thought. Existing BMIs have trade-offs across invasiveness, performance, spatial coverage spatiotemporal resolution. Functional ultrasound (fUS) neuroimaging is an emerging technology that balances these attributes may complement existing BMI recording technologies. In this study, we use fUS demonstrate a successful implementation of closed-loop ultrasonic BMI. We streamed data from the posterior parietal cortex two rhesus macaque monkeys while they performed eye hand movements. After training, controlled up eight movement directions using also developed method for pretraining previous sessions. This enabled immediate on subsequent days, even those occurred months apart, without requiring extensive recalibration. These findings establish feasibility BMIs, paving way new class less-invasive (epidural) generalize extended time periods promise restore function neurological impairments.

Language: Английский

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Cortico-basal ganglia plasticity in motor learning

Neuron, Journal Year: 2024, Volume and Issue: 112(15), P. 2486 - 2502

Published: July 12, 2024

Language: Английский

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A real-time, high-performance brain-computer interface for finger decoding and quadcopter control

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 8, 2024

People with paralysis express unmet needs for peer support, leisure activities, and sporting activities. Many within the general population rely on social media massively multiplayer video games to address these needs. We developed a high-performance finger brain-computer-interface system allowing continuous control of 3 independent groups 2D thumb movements. The was tested in human research participant over sequential trials requiring fingers reach hold targets, an average acquisition rate 76 targets/minute completion time 1.58 ± 0.06 seconds. Performance compared favorably previous animal studies, despite 2-fold increase decoded degrees-of-freedom (DOF). Finger positions were then used 4-DOF velocity virtual quadcopter, demonstrating functionality both fixed random obstacle courses. This approach shows promise controlling multiple-DOF end-effectors, such as robotic or digital interfaces work, entertainment, socialization.

Language: Английский

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Assistive sensory-motor perturbations influence learned neural representations

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 20, 2024

Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map activity into movement via a decoder. analyzed cortex as monkeys practiced BCI with decoder that adapted improve or maintain performance over days. The dimensionality of the population neurons controlling remained constant increased learning, counter expected trends from learning. Yet, time, information was contained in smaller subset modes. Moreover, ultimately stored modes occupied small fraction variance. An artificial network model suggests adaptive decoders contribute forming these compact representations. Our findings show assistive manipulate error for long-term learning computations, like credit assignment, informs our understanding has implications designing real-world BCIs.

Language: Английский

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Deep transfer learning-based decoder calibration for intracortical brain-machine interfaces

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 192, P. 110231 - 110231

Published: April 21, 2025

Language: Английский

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Stabilizing brain-computer interfaces through alignment of latent dynamics

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: May 19, 2025

Intracortical brain-computer interfaces (iBCIs) restore motor function to people with paralysis by translating brain activity into control signals for external devices. In current iBCIs, instabilities at the neural interface result in a degradation of decoding performance, which necessitates frequent supervised recalibration using new labeled data. One potential solution is use latent manifold structure that underlies population facilitate stable mapping between and behavior. Recent efforts unsupervised approaches have improved iBCI stability this principle; however, existing methods treat each time step as an independent sample do not account dynamics. Dynamics been used enable high-performance prediction movement intention, may also help improve stabilization. Here, we present platform Nonlinear Manifold Alignment (NoMAD), stabilizes recurrent network models NoMAD uses distribution alignment update nonstationary data consistent set dynamics, thereby providing input decoder. applications from monkey cortex collected during tasks, enables accurate behavioral unparalleled over weeks- months-long timescales without any recalibration.

Language: Английский

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Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 22, 2023

Abstract The neural population spiking activity recorded by intracortical brain-computer interfaces (iBCIs) contain rich structure. Current models of such are largely prepared for individual experimental contexts, restricting data volume to that collectable within a single session and limiting the effectiveness deep networks (DNNs). purported challenge in aggregating is pervasiveness context-dependent shifts distributions. However, large scale unsupervised pretraining nature spans heterogeneous data, has proven be fundamental recipe successful representation learning across learning. We thus develop Neural Data Transformer 2 (NDT2), spatiotemporal activity, demonstrate can leverage motor BCI datasets span sessions, subjects, tasks. NDT2 enables rapid adaptation novel contexts downstream decoding tasks opens path deployment pretrained DNNs iBCI control. Code: https://github.com/joel99/context_general_bci

Language: Английский

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