# Optimizing co-adaptation in motor BCIs by uncovering brain-decoder interactions > **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $695,214 ## Abstract Project Summary Brain-computer interfaces (BCIs) hold great promise to restore movement to paralyzed people. But BCIs cannot yet provide reliable performance across the long timespans and varied settings needed for real- world applications. Maintaining robust BCI performance over many days is challenging because brains are highly plastic. Plasticity during extended BCI practice leads to changes in how neural activity relates to move- ments—the brain’s encoding of BCI movement. How the brain’s encoding changes is influenced by the decod- ing algorithm used by the BCI to map neural activity into movement. These interactions create complex dynam- ics where methods that improve performance in the short term may produce problems longer-term. Indeed, our preliminary data suggests current adaptive decoding methods used to maintain performance over time lead the brain to form encoders where very few neural signals control movements, which make BCI vulnerable to cata- strophic failure with loss of a single neural signal. The long-term vision of this proposal is to expand the engi- neering tools available to produce robust, high-performance BCIs by building tools that account for and even leverage plasticity. To enable this vision, this proposal will test the overarching hypothesis that decoder-en- coder interactions can be used to jointly optimize BCI performance and robustness. We focus on robustness of BCI systems to signal loss and changes in task context. We will use an animal model where monkeys move cursors with activity from motor cortices, which has repeatedly informed clinical BCIs. We will leverage novel micro-electrocorticography implants that allow us to longitudinally monitor and manipulate cortical dynamics to advance our understanding of plasticity in multi-day (10 days) BCI training. We will test our overarching hy- pothesis across three aims. If our hypothesis is true, there must be relationships between decoders and prop- erties of encoders that are related to robustness. Aim 1 will determine whether decoders influence how infor- mation is structured in an encoder, which influences how robust BCIs are to signal loss. Aim 2 will determine whether decoders influence the specificity of learned encoders to BCI movements, which influences how ro- bust BCIs are to changes in tasks. Finally, if our hypothesis is true, it requires computational tools that can opti- mize multiple goals in a BCI. Aim 3 will test a novel decoder training paradigm we developed that can consider multiple objectives. We will compare our novel method to established single-objective methods to determine whether multi-objective methods can improve robustness without compromising performance. Across all aims, we will perform offline analyses and online perturbations to measure robustness to signal loss and changes in neural state and behavioral task. Together, these studies will identify how critical plasticity computations can be influenced through the decoder. Pairing t... ## Key facts - **NIH application ID:** 10935970 - **Project number:** 5R01NS134634-02 - **Recipient organization:** UNIVERSITY OF WASHINGTON - **Principal Investigator:** Amy L Orsborn - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $695,214 - **Award type:** 5 - **Project period:** 2023-09-25 → 2028-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10935970 ## Citation > US National Institutes of Health, RePORTER application 10935970, Optimizing co-adaptation in motor BCIs by uncovering brain-decoder interactions (5R01NS134634-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10935970. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*