ArticleNature biomedical engineering2023
Rapid adaptation of brain-computer interfaces to new neuronal ensembles or participants via generative modelling.
Article in Nature biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Motion Neural Monitoring Based on Flexible Materials: From Signal Acquisition to Training Enhancement.ACS omega · 2026Review
- Integrating metacognitive mechanisms optimizes EEG generative models via hierarchical regularization.iScience · 2026Article
- A Bidirectional Neural Interface With Direct On-Device Neuromorphic Decoding for Closed-Loop Optogenetics.bioRxiv : the preprint server for biology · 2026Article
- Application of biomimetic approaches in the treatment of neurological disorders.Materials today. Bio · 2025Review
- Soft Bioelectronic Interfaces for Continuous Peripheral Neural Signal Recording and Robust Cross-Subject Decoding.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Recent applications of EEG-based brain-computer-interface in the medical field.Military Medical Research · 2025Review
- Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding.Frontiers in neuroscience · 2025Article
- NeuroMotion: Open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement.PLoS computational biology · 2024Article
- Article
- A myoelectric digital twin for fast and realistic modelling in deep learning.Nature communications · 2023Article
- Neural Decoding for Intracortical Brain-Computer Interfaces.Cyborg and bionic systems (Washington, D.C.) · 2023Review
- Validation of a non-invasive, real-time, human-in-the-loop model of intracortical brain-computer interfaces.Journal of neural engineering · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
For brain-computer interfaces (BCIs), obtaining sufficient training data for algorithms that map neural signals onto actions can be difficult, expensive or even impossible. Here we report the development and use of a generative model-a model that synthesizes a virtually unlimited number of new data distributions from a learned data distribution-that learns mappings between hand kinematics and the associated neural spike trains. The generative spike-train synthesizer is trained on data from one recording session with a monkey performing a reaching task and can be rapidly adapted to new sessions or monkeys by using limited additional neural data. We show that the model can be adapted to synthesize new spike trains, accelerating the training and improving the generalization of BCI decoders. The approach is fully data-driven, and hence, applicable to applications of BCIs beyond motor control.
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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.