The Cognitive Skills Monitoring Task is a MEG -based problem that focuses on monitoring and assessing cognitive skills using Magnetoencephalography (MEG) data. The goal is to create a machine learning model that can accurately analyze MEG signals and classify cognitive states. The model attempts to detect and distinguish different cognitive states such as attention, relaxation, and baseline by recording and analyzing brain activity. This entails preprocessing the MEG data, extracting relevant features, and training a classification algorithm to recognize and classify cognitive states based on patterns in the MEG signals. The goal is to develop a dependable and efficient system for real-time cognitive skill monitoring that can be used in a variety of fields such as education, healthcare, and performance evaluation. In the field of cognitive neuroscience, the implemented simulation and visualization methodology provides a valuable tool for investigating cognitive processes and exploring the relationship between brain activity and cognitive states.