ArticleNature communications2025
Stabilizing brain-computer interfaces through alignment of latent dynamics.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Neural subspaces of imagined movements in parietal cortex remain stable over several years in humans.Journal of neural engineering · 2024Trial
- Restoring rapid natural bimanual typing with a neuroprosthesis after paralysis.Nature neuroscience · 2026Article
- Human learning of noninvasive brain-computer interfaces via manifold geometry.Nature neuroscience · 2026Article
- Article
- Long-term unsupervised recalibration of cursor-based intracortical brain-computer interfaces using a hidden Markov model.Nature biomedical engineering · 2026Article
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Brain-Computer Interfaces: The Dawn of a New Era in Disease Treatment.Exploration (Beijing, China) · 2026Review
- Review of Recent Advances in Implantable Brain-Computer Interfaces for the Restoration of Motor Function in Patients With Paralysis.Medical science monitor : international medical journal of experimental and clinical research · 2026Review
- Decoding intended speech with an intracortical brain-computer interface in a person with long-standing anarthria and locked-in syndrome.Cell reports · 2026Article
- Integrating living biomaterials into neuroelectronic systems.Biomedical engineering letters · 2026Review
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- Current status and future prospects of brain-computer interfaces in the field of neurological disease rehabilitation.Frontiers in rehabilitation sciences · 2026Review
- A theoretical framework for brain-computer interfaces: decodability, performance limits, and closed-loop adaptation.Frontiers in human neuroscience · 2026Article
- Cross-subject mapping of neural activity with restricted Boltzmann machines.Frontiers in computational neuroscience · 2026Article
- Long-term Intracortical Neural activity and Kinematics (LINK): An intracortical neural dataset for chronic brain-machine interfaces, neuroscience, and machine learning.Advances in neural information processing systems · 2025Article
- Decoding multi-joint hand movements from brain signals by learning a synergy-based neural manifold.Patterns (New York, N.Y.) · 2025Article
- The spatiotemporal structure of neural activity in motor cortex during reaching.bioRxiv : the preprint server for biology · 2025Article
- Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder.bioRxiv : the preprint server for biology · 2025Article
- Investigating the benefits of artificial neural networks over linear approaches to BMI decoding.Journal of neural engineering · 2025Article
- Article
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Authors and funding
13 authors.
Funding
Abstract
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 to use the latent manifold structure that underlies neural population activity to facilitate a stable mapping between brain activity and behavior. Recent efforts using unsupervised approaches have improved iBCI stability using this principle; however, existing methods treat each time step as an independent sample and do not account for latent dynamics. Dynamics have been used to enable high-performance prediction of movement intention, and may also help improve stabilization. Here, we present a platform for Nonlinear Manifold Alignment with Dynamics (NoMAD), which stabilizes decoding using recurrent neural network models of dynamics. NoMAD uses unsupervised distribution alignment to update the mapping of nonstationary neural data to a consistent set of neural dynamics, thereby providing stable input to the decoder. In applications to data from monkey motor cortex collected during motor tasks, NoMAD enables accurate behavioral decoding with unparalleled stability over weeks- to months-long timescales without any supervised recalibration.
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Registered trials
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.