Fusing motor neuroscience and artificial intelligence to create next-generation neural prostheses.

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ABSTRACT People with disabling motor disorders rely on assistive devices and caregivers for many of their most basic needs. Current assistive devices are inherently limited, as they rely on (and encumber) residual motor function as a command interface. Brain-machine interfaces (BMIs) provide a pathway to more powerful assistive options by directly monitoring brain activity and using it to decipher movement intention in real-time. However, BMIs have yet to achieve performance and robustness that would warrant widespread clinical adoption. A key obstacle is that nearly all BMIs to date use direct decoding, i.e., they attempt to map the activity of brain areas like motor cortex (MC) directly onto external movement parameters such as velocity. This has resulted in BMIs that are brittle: they often fail in new contexts, and are highly sensitive to neural interface instabilities. Instead, I envision a radically different approach with the potential to impact virtually every existing BMI application. The central element is dynamical systems decoding (DSD), a framework I developed that fuses advances in motor neuroscience with cutting-edge AI methods to achieve unprecedented decoding accuracy. DSD uses neural networks to precisely reveal MC's complex internal activity patterns, known as dynamics, on a moment-by- moment basis. This enables a clean separation between activity related to internal dynamics and activity related to external movement parameters. In offline analyses, I showed that DSD enables a breakthrough in decoding, predicting movements on millisecond timescales with substantially higher accuracy than the current state-of-the- art. A key focus of this proposal is developing universal, subject-independent BMIs that harness the remarkable similarities in MC dynamics observed across subjects. Using new AI methods to model more than a decade of previously-collected monkey data, we will test whether subject-independent models can enable BMIs that work nearly `out of the box', with performance that could only be achieved through massive datasets, while still avoiding burdensome, subject-specific calibration. In parallel with offline studies, we will work directly with people who are paralyzed to develop online BMIs with unparalleled performance and robustness. Performance improvements will be achieved through hybrid decoding paradigms that capitalize on high-level movement information that is uniquely uncovered via DSD. While BMI robustness is typically limited in direct decoding – due to gradual changes in the specific neurons being monitored – DSD will enable robust BMIs by leveraging MC dynamics, which are stable for years and independent of whichever specific neurons are being monitored at a given time. These two innovations would enable BMIs that achieve unprecedented performance and on- demand, 24/7 reliability for years. If successful, these studies will pave the way to dramatically improving the performance, robustness, and clinical utility of nearly every BMI application.