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Decoding executed and imagined grasping movements from distributed non-motor brain areas using a Riemannian decoder.

Maarten C Ottenhoff1, Maxime Verwoert1, Sophocles Goulis1

  • 1Department of Neurosurgery, Maastricht University, Maastricht, Netherlands.

Frontiers in Neuroscience
|December 11, 2023
PubMed
Summary

Brain activity from non-motor areas can decode executed and imagined movements using a Riemannian decoder. This approach enhances assistive tool control for individuals with physical impairments by leveraging distributed neural information.

Keywords:
Riemannian geometrybrain-computer interfacesdistributed recordingslow-dimensional representationmotor decoding

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Direct brain activity control of assistive tools can overcome muscular dysfunction and improve independence for physically impaired individuals.
  • While the motor cortex is a common target, decodable movement-related neural activity exists in non-motor brain regions.
  • Previous studies decoded movement from distributed areas individually, but combining information from multiple non-motor regions remained underexplored.

Purpose of the Study:

  • To investigate the decoding of executed and imagined movements from distributed non-motor brain areas using a Riemannian decoder.
  • To assess the performance of a Riemannian decoder on stereotactic-electroencephalographic (sEEG) data for movement decoding.
  • To highlight the distributed nature of movement-related neural activity beyond the motor cortex.

Main Methods:

  • Recorded neural activity from 8 epilepsy patients using sEEG during executed and imagined grasping tasks.
  • Excluded brain contacts located in or adjacent to the central sulcus before decoding.
  • Employed a Riemannian decoder to extract low-dimensional representations and classify movement using a minimum-distance-to-geometric-mean classifier.

Main Results:

  • Successfully decoded executed and imagined movements from distributed non-motor brain areas with an area under the receiver operator characteristic of 0.83 ± 0.11.
  • Demonstrated that no single brain area was the primary driver of decoding performance, underscoring the distributed nature of neural information.
  • Showcased the first application of a Riemannian decoder on sEEG data for decoding brain-wide activity outside the motor cortex.

Conclusions:

  • Movement-related neural activity is distributed across non-motor brain regions, offering potential for assistive technology.
  • A Riemannian decoder can effectively utilize sEEG data from distributed non-motor areas to decode executed and imagined movements.
  • Future research should explore motor-related neural activity beyond the motor cortex for enhanced brain-computer interfaces and assistive tools.