This thesis investigates the application of machine learning algorithms for the prediction of human gait kinematics utilizing electroencephalogram (EEG) signals, with an initial focus on healthy subjects to evaluate the potential of EEG data in estimating movement during ambulation. Specifically, the research aims to predict the angular dynamics of the joints by employing both EEG signals in isolation and integrating a limited number of kinematic samples. The accuracy of these predictions is assessed in terms of joint angle precision within a defined tolerance, as well as the model's capacity to differentiate between the stance and swing phases, which are crucial for a comprehensive understanding of the gait cycle.