ArticleNature communications2016
Making brain-machine interfaces robust to future neural variability.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 86 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.
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Who cites it
86 citing papers in PubMed.
- Speech motor cortex enables BCI cursor control and click.Journal of neural engineering · 2025Trial
- How different immersive environments affect intracortical brain computer interfaces.Journal of neural engineering · 2025Trial
- Speech-related dorsal motor cortex activity does not interfere with iBCI cursor control.Journal of neural engineering · 2020Trial
- Human learning of noninvasive brain-computer interfaces via manifold geometry.Nature neuroscience · 2026Article
- In conversation with Sergey Stavisky.Nature neuroscience · 2026Article
- Long-term unsupervised recalibration of cursor-based intracortical brain-computer interfaces using a hidden Markov model.Nature biomedical engineering · 2026Article
- Long-term independent use of an intracortical brain-computer interface for speech and cursor control.Nature medicine · 2026Article
- Duration-modulated neural population dynamics in humans during BMI controls.Communications biology · 2026Article
- Intracortical BCI Performance is Robust to Changes in Attentional Load During Dual-Tasking.bioRxiv : the preprint server for biology · 2026Article
- A theoretical framework for brain-computer interfaces: decodability, performance limits, and closed-loop adaptation.Frontiers in human 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
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- Brain-computer interface control with artificial intelligence copilots.Nature machine intelligence · 2025Article
- Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder.bioRxiv : the preprint server for biology · 2025Article
- Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participants.medRxiv : the preprint server for health sciences · 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
- Stabilizing brain-computer interfaces through alignment of latent dynamics.Nature communications · 2025Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training data perturbations. We developed a new multiplicative recurrent neural network BMI decoder that successfully learned a large variety of neural-to-kinematic mappings and became more robust with larger training data sets. Here we demonstrate that when tested with a non-human primate preclinical BMI model, this decoder is robust under conditions that disabled a state-of-the-art Kalman filter-based decoder. These results validate a new BMI strategy in which accumulated data history are effectively harnessed, and may facilitate reliable BMI use by reducing decoder retraining downtime.
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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.