[An improved maximal information coefficient algorithm applied in the analysis of functional corticomuscular coupling...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Quantifying functional coupling between the motor cortex and muscles is crucial for understanding movement control.
• The Maximal Information Coefficient (MIC) algorithm effectively measures neural signal coupling but is computationally intensive.
• Existing methods for assessing corticomuscular coupling (FCMC) may lack speed and precision.

Purpose of the Study:

• To develop an improved, computationally efficient algorithm for accurately quantifying the coupling between electroencephalogram (EEG) and surface electromyography (sEMG) signals.
• To assess the functional corticomuscular coupling in stroke patients during motor tasks.
• To establish a novel quantitative method for evaluating motor function in stroke survivors.

Main Methods:

• An improved Maximal Information Coefficient (MIC) algorithm was developed, integrating K-means++ clustering for efficient nonlinear time series analysis.
• The improved MIC algorithm was validated using simulations under varying noise conditions.
• EEG and sEMG signals were recorded during right dorsiflexion in stroke patients and healthy controls.

Main Results:

• Simulations demonstrated that the improved MIC algorithm accurately and rapidly captures coupling in nonlinear time series, even with noise.
• The improved method successfully quantified EEG-sEMG coupling in specific frequency bands in stroke patients.
• Stroke patients exhibited significantly weaker functional corticomuscular coupling in the beta (14-30 Hz) and gamma (31-45 Hz) bands compared to controls.
• Beta-band MIC values showed a positive correlation with Fugl-Meyers Assessment (FMA) scores.

Conclusions:

• The improved MIC algorithm offers a fast and accurate approach for quantifying nonlinear time series coupling, particularly for neural signals.
• Reduced functional corticomuscular coupling in beta and gamma bands is a key characteristic of motor impairment in stroke patients.
• This novel method holds promise as a quantitative tool for assessing motor function and recovery in stroke rehabilitation.