Stabilization of a brain–computer interface via the alignment of low-dimensional spaces of neural activity
Open accessAbstract
The instability of neural recordings can render clinical brain-computer interfaces (BCIs) uncontrollable. Here, we show that the alignment of low-dimensional neural manifolds (low-dimensional spaces that describe specific correlation patterns between neurons) can be used to stabilize neural activity, thereby maintaining BCI performance in the presence of recording instabilities. We evaluated the stabilizer with non-human primates during online cursor control via intracortical BCIs in the presence of severe and abrupt recording instabilities. The stabilized BCIs recovered proficient control under different instability conditions and across multiple days. The stabilizer does not require knowledge of user intent and can outperform supervised recalibration. It stabilized BCIs even when neural activity contained little information about the direction of cursor movement. The stabilizer may be applicable to other neural interfaces and may improve the clinical viability of BCIs.
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The manifold-stabilization approach has mainly been applied in upper limb decoders.
“This approach has primarily been applied in the context of upper limb decoders103 but may scale to speech neuroprostheses in the future.”
From The speech neuroprosthesis (2024) · cites this paper as [103]
Models that learn an underlying manifold structure for a behaviour can be used to stabilize MEA activity over time.
“One line of work involves developing models that learn an underlying manifold (low-dimensional spaces that capture neural population dynamics) structure for a given behaviour103,104,121, which can then be used to stabilize MEA activity over time.”
From The speech neuroprosthesis (2024) · cites this paper as [103]
Unsupervised algorithms for updating decoders to neural changes should be feasible.
“Furthermore, we found that neural activity changed at a gradual rate over time, suggesting that unsupervised algorithms for updating decoders to neural changes should be feasible24–27 (Fig. 4d).”
From A high-performance speech neuroprosthesis (2023) · cites this paper as [26]
These results indicate that substantial gains in performance are probably still possible with further language model improvements and more robust decoding algorithms that generalize better to non-stationary data.
“These results indicate that substantial gains in performance are probably still possible with further language model improvements and more robust decoding algorithms that generalize better to non-stationary data (for example, unsupervised methods that track non-stationarities without the requirement for new training data24–27).”
From A high-performance speech neuroprosthesis (2023) · cites this paper as [26]
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References 74 · Cited by 212
- Authors
- Degenhart, A. D., Bishop, W. E., Oby, E. R., Tyler-Kabara, E. C., Chase, S. M., Batista, A. P., & Yu, B. M.
- Affiliations
- University of Pittsburgh (US)
- Howard Hughes Medical Institute (US)
- Center for the Neural Basis of Cognition (US)
- Corresponding author
- Yu, Byron M.
- Venue
- Nature Biomedical Engineering
- Published
- 2020-04-20
- Volume
- 4
- Issue
- 7
- Pages
- 672-685
- ISSN
- 2157-846X (ISSN-L)
- PMC
- PMC7822646
- OpenAlex
- W3016977963
- Cited by
- 212
- References
- 74
- Citations by year
- 2026: 31 · 2025: 49 · 2024: 35 · 2023: 36 · 2022: 30 · 2021: 25 · 2020: 6
- FWCI
- 13.03
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- Locations
- Nature Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institute of Neurological Disorders and Stroke (R01NS105318, T32NS086749)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD071686)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- brain-computer interface, intracortical recording, non-human primates, cursor control, neural decoding
- MeSH terms
- Animals, Behavior, Animal, Electrodes, Electroencephalography, Electrophysiology, Macaca mulatta, Male, Motor Cortex, Movement, Neurons, User-Computer Interface, Brain-Computer Interfaces
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