ArticleNature communications2022
Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed, 97 citations in OpenAlex.
- Prioritized nonlinear modeling of shared dynamics across neural populations.Journal of neural engineering · 2026Article
- Assistive algorithms influence neural representations in motor brain-computer interfaces.Nature communications · 2026Article
- Duration-modulated neural population dynamics in humans during BMI controls.Communications biology · 2026Article
- The Association of Primate Veterinarians and the European Primate Veterinarians Position Statement on the Value of Nonhuman Primates in Research.Journal of the American Association for Laboratory Animal Science : JAALAS · 2026Article
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A Bidirectional Neural Interface With Direct On-Device Neuromorphic Decoding for Closed-Loop Optogenetics.bioRxiv : the preprint server for biology · 2026Article
- A general model based on Riemannian manifold for stable decoding movement trajectory from ECoG signals.iScience · 2026Article
- Multi-Channel Neural Interface for Neural Recording and Neuromodulation.Small methods · 2026Review
- Brain-aligning of semantic vectors improves neural decoding of visual stimuli.Communications biology · 2026Article
- Employing feedforward backpropagated neural network for Doppler scale estimation in underwater acoustic CP-OFDM communication.Scientific reports · 2025Article
- Visual context affects the perceived timing of tactile sensations elicited through intracortical microstimulation: a case study of two participants.Journal of neurophysiology · 2025Article
- Neural trajectories improve motor precision.bioRxiv : the preprint server for biology · 2025Article
- Gesture encoding in human left precentral gyrus neuronal ensembles.Communications biology · 2025Article
- EEG-based brain-computer interface enables real-time robotic hand control at individual finger level.Nature communications · 2025Article
- Investigating the benefits of artificial neural networks over linear approaches to BMI decoding.Journal of neural engineering · 2025Article
- Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.Nature biomedical engineering · 2025Article
- Pseudo-linear summation explains neural geometry of multi-finger movements in human premotor cortex.Nature communications · 2025Article
- Metaplasticity and continual learning: mechanisms subserving brain computer interface proficiency.Journal of neural engineering · 2025Article
- EEG Baseline Analysis for Effective Extraction of P300 Event-Related Potentials for Welfare Interfaces.Sensors (Basel, Switzerland) · 2025Article
- Replacement as an aging intervention.Nature aging · 2025Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to a control signal for the prosthetic device is one of the limitations in achieving rapid and realistic finger movements. To achieve more realistic finger movements, we developed a shallow feed-forward neural network to decode real-time two-degree-of-freedom finger movements in two adult male rhesus macaques. Using a two-step training method, a recalibrated feedback intention-trained (ReFIT) neural network is introduced to further improve performance. In 7 days of testing across two animals, neural network decoders, with higher-velocity and more natural appearing finger movements, achieved a 36% increase in throughput over the ReFIT Kalman filter, which represents the current standard. The neural network decoders introduced herein demonstrate real-time decoding of continuous movements at a level superior to the current state-of-the-art and could provide a starting point to using neural networks for the development of more naturalistic brain-controlled prostheses.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.