ArticleScientific reports2019
Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00912041 (BrainGate2), which is not on this map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
BrainGate2: Feasibility Study of an Intracortical Neural Interface System for Persons With Tetraplegia
Who cites it
20 citing papers in PubMed.
- Speech motor cortex enables BCI cursor control and click.Journal of neural engineering · 2025Trial
- Long-term unsupervised recalibration of cursor-based intracortical brain-computer interfaces using a hidden Markov model.Nature biomedical engineering · 2026Article
- Closed-loop error damping in human BCI using pre-error motor cortex activity.bioRxiv : the preprint server for biology · 2026Article
- Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.Nature biomedical engineering · 2025Article
- Less is more: selection from a small set of options improves BCI velocity control.Journal of neural engineering · 2025Article
- Speech motor cortex enables BCI cursor control and click.bioRxiv : the preprint server for biology · 2024Article
- Less is more: selection from a small set of options improves BCI velocity control.bioRxiv : the preprint server for biology · 2024Article
- From monkeys to humans: observation-basedEMGbrain-computer interface decoders for humans with paralysis.Journal of neural engineering · 2023Article
- Decoding hand kinetics and kinematics using somatosensory cortex activity in active and passive movement.iScience · 2023Article
- Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity.bioRxiv : the preprint server for biology · 2023Article
- Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations.Journal of child neurology · 2023Review
- Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder.Nature communications · 2022Article
- The science and engineering behind sensitized brain-controlled bionic hands.Physiological reviews · 2022Review
- Closed-loop motor imagery EEG simulation for brain-computer interfaces.Frontiers in human neuroscience · 2022Article
- Real-time synthesis of imagined speech processes from minimally invasive recordings of neural activity.Communications biology · 2021Article
- An artificial intelligence that increases simulated brain-computer interface performance.Journal of neural engineering · 2021Article
- Summary of over Fifty Years with Brain-Computer Interfaces-A Review.Brain sciences · 2021Review
- Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling.Frontiers in computational neuroscience · 2021Article
- The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models.Neural computation · 2020Article
- Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning.Frontiers in computational neuroscience · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
Decoders optimized offline to reconstruct intended movements from neural recordings sometimes fail to achieve optimal performance online when they are used in closed-loop as part of an intracortical brain-computer interface (iBCI). This is because typical decoder calibration routines do not model the emergent interactions between the decoder, the user, and the task parameters (e.g. target size). Here, we investigated the feasibility of simulating online performance to better guide decoder parameter selection and design. Three participants in the BrainGate2 pilot clinical trial controlled a computer cursor using a linear velocity decoder under different gain (speed scaling) and temporal smoothing parameters and acquired targets with different radii and distances. We show that a user-specific iBCI feedback control model can predict how performance changes under these different decoder and task parameters in held-out data. We also used the model to optimize a nonlinear speed scaling function for the decoder. When used online with two participants, it increased the dynamic range of decoded speeds and decreased the time taken to acquire targets (compared to an optimized standard decoder). These results suggest that it is feasible to simulate iBCI performance accurately enough to be useful for quantitative decoder optimization and design.
Indexed as
Identifiers
What OpenQuestion holds
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.