A cryptography-based approach for movement decoding
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Abstract
Brain decoders use neural recordings to infer the activity or intent of a user. To train a decoder, one generally needs to infer the measured variables of interest (covariates) from simultaneously measured neural activity. However, there are cases for which obtaining supervised data is difficult or impossible. Here, we describe an approach for movement decoding that does not require access to simultaneously measured neural activity and motor outputs. We use the statistics of movement-much like cryptographers use the statistics of language-to find a mapping between neural activity and motor variables, and then align the distribution of decoder outputs with the typical distribution of motor outputs by minimizing their Kullback-Leibler divergence. By using datasets collected from the motor cortex of three non-human primates performing either a reaching task or an isometric force-production task, we show that the performance of such a distribution-alignment decoding algorithm is comparable to the performance of supervised approaches. Distribution-alignment decoding promises to broaden the set of potential applications of brain decoding.
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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 [24]
The Results describe DAD as an unsupervised decoder that worked across recording days without changing model parameters, supporting its use without new paired training data. But the paper does not show the method tracking non-stationarities; the Discussion treats accounting for drift in neural properties as a potential improvement not included in DAD. (LLM, conf=0.82)
Experimental — accuracy not yet measuredUnsupervised 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 [24]
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- Authors
- Dyer, E. L., Gheshlaghi Azar, M., Perich, M. G., Fernandes, H. L., Naufel, S., Miller, L. E., & Körding, K. P.
- Affiliations
- Georgia Institute of Technology (US)
- Northwestern University (US)
- Corresponding author
- Dyer, Eva L.
- Venue
- Nature Biomedical Engineering
- Published
- 2017-12-12
- Volume
- 1
- Issue
- 12
- Pages
- 967-976
- ISSN
- 2157-846X (ISSN-L)
- PMC
- PMC8376093
- OpenAlex
- W2952375713
- Cited by
- 70
- References
- 46
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 14 · 2023: 12 · 2022: 6 · 2021: 12 · 2020: 8 · 2019: 3 · 2018: 3 · 2017: 2
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Biomedical Engineering (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R01MH103910, U01MH109100)
- National Institute of Neurological Disorders and Stroke (R01NS074044, R01NS053603)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- brain decoding, movement decoding, distribution alignment, Kullback-Leibler divergence, motor cortex, neuronal activity, reaching task, isometric force production, non-human primates
- MeSH terms
- Machine Learning, Algorithms, Animals, Data Interpretation, Statistical, Macaca mulatta, Models, Neurological, Motor Cortex, Movement, Neurons, Brain-Computer Interfaces
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