ArticleeLife2023
Using adversarial networks to extend brain computer interface decoding accuracy over time.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Long-term unsupervised recalibration of cursor-based intracortical brain-computer interfaces using a hidden Markov model.Nature biomedical engineering · 2026Article
- A general model based on Riemannian manifold for stable decoding movement trajectory from ECoG signals.iScience · 2026Article
- Optimal sensor set for MEG-based spontaneous and intended speech decoding toward practical communication.Frontiers in human neuroscience · 2026Article
- Brain-computer interfaces: an engineering black-box swindle or a lone advance guided by deep learning.Frontiers in neuroscience · 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
- Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder.bioRxiv : the preprint server for biology · 2025Article
- Stabilizing brain-computer interfaces through alignment of latent dynamics.Nature communications · 2025Article
- Less is more: selection from a small set of options improves BCI velocity control.Journal of neural engineering · 2025Article
- A Generalist Intracortical Motor Decoder.bioRxiv : the preprint server for biology · 2025Article
- Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.Journal of neural engineering · 2025Article
- Decoding Continuous Tracking Eye Movements from Cortical Spiking Activity.International journal of neural systems · 2025Article
- Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling.Advances in neural information processing systems · 2025Article
- Reducing power requirements for high-accuracy decoding in iBCIs.Journal of neural engineering · 2024Article
- Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.Communications biology · 2024Article
- A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs.Scientific reports · 2024Article
- Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.bioRxiv : the preprint server for biology · 2024Article
- Initial and corrective submovement encoding differences within primary motor cortex during precision reaching.Journal of neurophysiology · 2024Article
- Event detection and classification from multimodal time series with application to neural data.Journal of neural engineering · 2024Article
- Brain-Computer Interfaces with Intracortical Implants for Motor and Communication Functions Compensation: Review of Recent Developments.Sovremennye tekhnologii v meditsine · 2024Review
- Decoding motor plans using a closed-loop ultrasonic brain-machine interface.Nature neuroscience · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the 'decoder' at the heart of the iBCI typically degrades over time due to turnover of recorded neurons. To compensate, decoders can be recalibrated, but this requires the user to spend extra time and effort to provide the necessary data, then learn the new dynamics. As the recorded neurons change, one can think of the underlying movement intent signal being expressed in changing coordinates. If a mapping can be computed between the different coordinate systems, it may be possible to stabilize the original decoder's mapping from brain to behavior without recalibration. We previously proposed a method based on Generalized Adversarial Networks (GANs), called 'Adversarial Domain Adaptation Network' (ADAN), which aligns the distributions of latent signals within underlying low-dimensional neural manifolds. However, we tested ADAN on only a very limited dataset. Here we propose a method based on Cycle-Consistent Adversarial Networks (Cycle-GAN), which aligns the distributions of the full-dimensional neural recordings. We tested both Cycle-GAN and ADAN on data from multiple monkeys and behaviors and compared them to a third, quite different method based on Procrustes alignment of axes provided by Factor Analysis. All three methods are unsupervised and require little data, making them practical in real life. Overall, Cycle-GAN had the best performance and was easier to train and more robust than ADAN, making it ideal for stabilizing iBCI systems over time.
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