ArticleCommunications biology2024
Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Restoring rapid natural bimanual typing with a neuroprosthesis after paralysis.Nature neuroscience · 2026Article
- Decoding intended speech with an intracortical brain-computer interface in a person with long-standing anarthria and locked-in syndrome.Cell reports · 2026Article
- Closed-loop error damping in human BCI using pre-error motor cortex activity.bioRxiv : the preprint server for biology · 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
- Metaplasticity and continual learning: mechanisms subserving brain computer interface proficiency.Journal of neural engineering · 2025Article
- Invasive Brain-Computer Interface for Communication: A Scoping Review.Brain sciences · 2025Review
- Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding.Frontiers in neuroscience · 2025Article
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11 authors.
Funding
Abstract
Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method, "MINDFUL", to measure instabilities in neural data for useful long-term iBCI, without needing labels of user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.
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