Synchronous analysis of brain regions based on multi-scale permutation transfer entropy

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Brain region coupling, assessed via electroencephalogram (EEG) signals, is crucial for understanding motor function, especially in post-stroke recovery.
• Quantifying interactions between brain areas is essential for developing effective rehabilitation strategies.

Purpose of the Study:

• To introduce and utilize multi-scale permutation transfer entropy (MPTE) for characterizing EEG signal coupling between bilateral motor and sensory areas.
• To investigate differences in brain coupling between post-stroke patients and healthy controls during a hand grip task.

Main Methods:

• Recruited 5 post-stroke patients and 6 healthy volunteers for a hand grip task with varying contraction levels.
• Measured EEG signals from bilateral motor and sensory areas.
• Computed and analyzed multi-scale permutation transfer entropy (MPTE) across different frequency bands.

Main Results:

• Healthy controls exhibited bi-directional motor-sensory coupling, strongest in the beta band, particularly in the dominant hand.
• Coupling strength decreased with increased contraction intensity in healthy individuals.
• Stroke patients demonstrated significantly weaker beta-band MPTE compared to healthy controls.

Conclusions:

• MPTE effectively quantifies coupling properties between multiple brain regions using EEG signals.
• This method offers a promising approach for studying the neural mechanisms underlying functional motor recovery after stroke.
• Findings highlight potential biomarkers for assessing motor deficits and recovery in stroke patients.