Data and code from: Human learning of noninvasive brain-computer interfaces via manifold geometry

Brain-computer interfaces (BCIs) promise to restore and enhance a wide range of human capabilities. However, a barrier to the adoption of BCIs is that learning to control them can be difficult and slow, with inconsistent results across users. We hypothesized that human BCI learning could be accelerated by leveraging the naturally occurring geometric structure of brain activity, or its intrinsic manifold, extracted using a data-diffusion process. We trained participants on a noninvasive BCI that allowed them to gain real-time control of an avatar in a virtual reality game by modulating functional magnetic resonance imaging (fMRI) activity in brain regions that support spatial navigation. We then perturbed the mapping between fMRI activity patterns and the movement of the avatar to test our manifold hypothesis. When the new mapping relied on directions of significant variance along the intrinsic manifold, participants succeeded in regaining control of the BCI by aligning their brain activ..., , # Dataset from \"Human learning of noninvasive brain-computer interfaces via manifold geometry\"

Busch, E. L., Fincke, E. C., Lajoie, G., Krishnaswamy, S., & Turk-Browne, N. B. (2026). Human learning of noninvasive brain-computer interfaces via manifold geometry. *Nature Neuroscience*.

This directory contains raw and preprocessed neuroimaging and behavioral data used for the analyses in the manuscript. To analyze this data, please refer to [https://github.com/ericabusch/avatarRT_analysis](https://github.com/ericabusch/avatarRT_analysis). The analysis notebook at that repository utilizes these files to recreate the figures reported in the paper. For this purpose, the contents of this directory are expected to be placed in a folder called `data/avatarRT`. The notebook can be adapted to explore other analyses, which in some cases will create new files.

All files with the suffix `.nii.gz` or `.nii` can be opened using FSL ([https://fsl.fmrib.ox.ac.uk/fsl/fslwiki](https://fsl.fmrib.ox.ac.uk..., All participants explicitly consented to publishing de-identified data in the public domain. To de-identify the fMRI data, raw DICOM files were converted to NIfTI format using dcm2niix, which scrubbed identifying header information during conversion. Facial features were removed from scans through skull stripping with FSL's BET (Brain Extraction Tool), eliminating the risk of face reconstruction from high-resolution anatomical images,