Neural signal analysis in chronic stroke: advancing intracortical brain-computer interface design

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.