Young Scientist Fellow, Institute for Basic Science CNIR @ Sungkyunkwan University
Incoming Assistant Professor, CCC @ Vanderbilt University
Prev. Yale & Dartmouth | Parent of a Very Good Dog | ericabusch.github.io
Erica Busch
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For motor/communication disorders, manifold-aligned interfaces could work for far more people. For mental health, incremental manifold-aware approaches may outperform blunter interventions. More broadly: understanding the natural structure of our minds may be key to improving them.
When the mapping respected the manifold, people learned fast. When it didn't, learning stalled entirely — no improvement in the same amount of training time.
BCIs have been around for decades, but about 1/3 of users never gain control at all, regardless of practice. We think this struggle isn't a facet of effort, but a design problem: BCIs asked the brain to generate activity that's entirely unnatural & don't provide the support for improvement.
We designed three BCI mappings: one following the brain's most natural patterns, one using less dominant but still natural patterns, and one requiring patterns the brain doesn't naturally produce.
BCI learning changed the brain itself. Neural activity reorganized to align with the interface's goals & the degree of reorganization predicted individual differences in learning. Effects rippled beyond the targeted regions — suggesting BCI learning reshapes neural architecture more broadly.
Accordingly, the manifold determines both what people can learn and how fast. Our findings dovetail beautifully with work across species and recording methods, pointing to broad principles of how the brain learns. The manifold isn't just a neuroscience curiosity — it has real implications.
This project started in 2020 & shaped my PhD, so it's especially exciting to see it out. Massive thanks to my coauthors @glajoie.bsky.social & @krishnaswamylab.bsky.social & everyone who helped collect this massive dataset. news.yale.edu/2026/06/09/brain-computer-interface-works-not-against-brain
Erica Busch
Brain activity doesn't wander randomly through high-D space. It travels along well-worn routes, forming a low-D structures or neural manifold. Using T-PHATE, which we developed previously (nature.com/articles/s43588-023-00419-0), we learned each person's individual manifold prior to BCI training.
Our new paper is out this week in Nature Neuroscience! www.nature.com/articles/s41... We built a BCI that works with the brain's natural geometry — and we found that people could learn to play a video game with their brains in <1 hr of training. This efficiency is groundbreaking & here's why:
Erica Busch
Erica Busch
Erica Busch
Erica Busch
Erica Busch
Busch et al. use nonlinear neural manifolds to help humans gain rapid control over a noninvasive brain–computer interface, allowing them to learn how to play a video game with real-time fMRI neurofeed...
