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Updated: May 23, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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Neural signal analysis in chronic stroke: advancing intracortical brain-computer interface design.

Nabila Shawki1, Alessandro Napoli1, Carlos E Vargas-Irwin2,3

  • 1Raphael Center for Neurorestoration, Thomas Jefferson University, Philadelphia, PA, United States.

Frontiers in Human Neuroscience
|March 10, 2025
PubMed
Summary

Intracortical brain-computer interfaces (iBCIs) can decode motor intentions from stroke-affected brains. This research demonstrates the potential of iBCIs for restoring function in individuals with subcortical strokes.

Keywords:
intracortical brain-computer interfacesmicroelectrode arraymotor control restorationneuromodulationneuroprostheticneurorehabilitationneurotechnologystroke

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Intracortical brain-computer interfaces (iBCIs) offer a promising avenue for functional restoration in stroke survivors.
  • The efficacy of iBCIs in neocortical areas affected by subcortical strokes (impacting white matter and basal ganglia) remains largely unexplored.

Purpose of the Study:

  • To investigate the feasibility of decoding neural signals from a stroke-affected brain using iBCIs.
  • To analyze electrophysiological activities in the stroke-affected neocortex to inform the design of assistive medical devices.

Main Methods:

  • Decoding of local field potentials (LFPs) and spikes from intracortical electrode arrays in a chronic subcortical stroke patient.
  • Analysis of neural signals during motor tasks with and without a powered orthosis.
  • Frequency domain analysis and investigation of cross-channel neural firing patterns.

Main Results:

  • Neural signal analysis revealed distinct frequency power shifts correlated with proximity to the stroke site.
  • Observed coordinated neural firing during motor tasks and synchronized bursts during relaxation.
  • Identified three key features for decoding motor intentions from stroke-affected neural data.

Conclusions:

  • Motor intents can be successfully decoded from neural signals in a subcortical stroke-affected brain, despite unique neural activity patterns.
  • Findings support the potential of iBCIs for functional recovery in stroke patients with specific brain lesions.