ArticleAdvances in neural information processing systems2024
Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces.
Article in Advances in neural information processing systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Prioritized nonlinear modeling of shared dynamics across neural populations.Journal of neural engineering · 2026Article
- Review of Recent Advances in Implantable Brain-Computer Interfaces for the Restoration of Motor Function in Patients With Paralysis.Medical science monitor : international medical journal of experimental and clinical research · 2026Review
- Closed-loop error damping in human BCI using pre-error motor cortex activity.bioRxiv : the preprint server for biology · 2026Article
- Context-aware temporal synthesis for scene, entity, and event inference from silent image.Frontiers in neuroscience · 2026Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
People with brain or spinal cord-related paralysis often need to rely on others for basic tasks, limiting their independence. A potential solution is brain-machine interfaces (BMIs), which could allow them to voluntarily control external devices (e.g., robotic arm) by decoding brain activity to movement commands. In the past decade, deep-learning decoders have achieved state-of-the-art results in most BMI applications, ranging from speech production to finger control. However, the 'black-box' nature of deep-learning decoders could lead to unexpected behaviors, resulting in major safety concerns in real-world physical control scenarios. In these applications, explainable but lower-performing decoders, such as the Kalman filter (KF), remain the norm. In this study, we designed a BMI decoder based on KalmanNet, an extension of the KF that augments its operation with recurrent neural networks to compute the Kalman gain. This results in a varying "trust" that shifts between inputs and dynamics. We used this algorithm to predict finger movements from the brain activity of two monkeys. We compared KalmanNet results offline (pre-recorded data,
Identifiers
40231170PMC11996206What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.