ArticleJournal of neural engineering2017
Augmenting intracortical brain-machine interface with neurally driven error detectors.
Article in Journal of neural engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Preparatory encoding of intended movement in the human motor cortex and implications for brain-computer interfaces.Current biology : CB · 2026Article
- Duration-modulated neural population dynamics in humans during BMI controls.Communications biology · 2026Article
- Closed-loop error damping in human BCI using pre-error motor cortex activity.bioRxiv : the preprint server for biology · 2026Article
- Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder.bioRxiv : the preprint server for biology · 2025Article
- Error encoding in human speech motor cortex.bioRxiv : the preprint server for biology · 2025Article
- Differential motor neuron activity in rats during successful and failed grasping.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Reward signals in the motor cortex: from biology to neurotechnology.Nature communications · 2025Review
- Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex.Nature communications · 2023Article
- Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity.bioRxiv : the preprint server for biology · 2023Article
- Error detection and correction in intracortical brain-machine interfaces controlling two finger groups.Journal of neural engineering · 2023Article
- Article
- Power-saving design opportunities for wireless intracortical brain-computer interfaces.Nature biomedical engineering · 2020Article
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- Structure and variability of delay activity in premotor cortex.PLoS computational biology · 2019Article
- Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveMaking mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs. APPROACH: We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task. MAIN
resultsWe decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error 'detect-and-act' system that attempts to automatically 'undo' or 'prevent' mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF). SIGNIFICANCE: Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.
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Registered trials
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