Decoding Motor Decision-Making Patterns: An EEG and EMG Connectivity Modeling Approach

Cognitive workload is a widely used concept for addressing the balance between task demand and the subject's available resources to complete the task. Electrophysiological techniques have been used to take a quantitative approach. In this study, we assessed motor performance and functional connectivity using EEG and EMG recordings to develop multiple models for motor planning during a motor decision-making task. The aim of the present work was to compare the performance of these models in evaluating the motor decision-making process. We conducted a motor reaction task where the subjects had to decide which hand should move based on a visual stimulus. We computed functional connectivity between cortex and muscles, and muscles between them using corticomuscular coherence and intermuscular coherence. Three models were generated, only one demonstrating strong performance. These results have revealed two different types of motor decision-making processes depending on the hand that is to be moved. This research contributes to the understanding of motor decision-making processes and provides insights into the evaluation of cognitive workload.