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Human learning of noninvasive brain-computer interfaces via manifold geometry.

Erica L Busch1,2, E Chandra Fincke1, Guillaume Lajoie3,4

  • 1Department of Psychology, Yale University, New Haven, CT, USA.

Nature Neuroscience
|June 9, 2026
PubMed
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Brain-computer interfaces (BCIs) improve with learning that leverages brain activity geometry. Understanding neural manifolds accelerates BCI adoption for restoring human capabilities.

Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Brain-computer interfaces (BCIs) offer potential for restoring and enhancing human capabilities.
  • BCI adoption is hindered by slow and inconsistent user learning.
  • Existing BCI paradigms do not fully utilize the inherent structure of neural activity.

Purpose of the Study:

  • To investigate if leveraging the intrinsic geometry of brain activity can accelerate BCI learning.
  • To determine how neural manifolds constrain learning in real-time functional magnetic resonance imaging (rtfMRI)-BCI tasks.
  • To identify principles for improving neurotechnologies by understanding brain activity geometry.

Main Methods:

  • Participants trained using rtfMRI to control a video game avatar via self-modulation of spatial navigation brain regions.

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Last Updated: Jun 11, 2026

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  • Data diffusion techniques were employed to extract the intrinsic manifold of brain activity.
  • The mapping between brain activity and avatar movement was perturbed to assess the influence of neural manifolds on learning.

    • Main Results:

    • BCI learning was significantly accelerated when new control mappings aligned with directions of high variance on the intrinsic neural manifold.
    • Participants could successfully learn to control the avatar by realigning brain activity along manifold-guided directions.
    • Learning failed when new mappings deviated from the intrinsic manifold structure, indicating its constraining role.

    Conclusions:

    • The intrinsic geometry of brain activity, or neural manifold, plays a crucial role in guiding human learning of complex cognitive tasks within BCIs.
    • Leveraging neural manifold geometry offers a promising principle for enhancing the speed and consistency of BCI learning.
    • This research identifies a fundamental mechanism for improving the design and efficacy of future neurotechnologies.