ArticleNature biomedical engineering2017
A cryptography-based approach for movement decoding.
Article in Nature biomedical engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Long-term unsupervised recalibration of cursor-based intracortical brain-computer interfaces using a hidden Markov model.Nature biomedical engineering · 2026Article
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- 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
- Stabilizing brain-computer interfaces through alignment of latent dynamics.Nature communications · 2025Article
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- Dimensionality reduction beyond neural subspaces with slice tensor component analysis.Nature neuroscience · 2024Article
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- A high-performance speech neuroprosthesis.Nature · 2023Article
- Aligning latent representations of neural activity.Nature biomedical engineering · 2023Article
- Rapid adaptation of brain-computer interfaces to new neuronal ensembles or participants via generative modelling.Nature biomedical engineering · 2023Article
- How our understanding of memory replay evolves.Journal of neurophysiology · 2023Review
- Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers.Advances in neural information processing systems · 2022Article
- The science and engineering behind sensitized brain-controlled bionic hands.Physiological reviews · 2022Review
- Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control.Frontiers in human neuroscience · 2022Review
- Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity.Advances in neural information processing systems · 2021Article
- Go with the FLOW: visualizing spatiotemporal dynamics in optical widefield calcium imaging.Journal of the Royal Society, Interface · 2021Article
- Brain microvasculature has a common topology with local differences in geometry that match metabolic load.Neuron · 2021Article
- A comprehensive model-based framework for optimal design of biomimetic patterns of electrical stimulation for prosthetic sensation.Journal of neural engineering · 2020Article
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Authors and funding
7 authors.
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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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