Brain–Computer Interface (BCI) systems create a direct communication channel between the human brain and external devices, bypassing conventional neuromuscular pathways. These systems interpret brain activity typically captured via electroencephalography (EEG) to infer user intent and execute commands accordingly. In this study, we focus on the classification of motor imagery (MI) signals, a widely used paradigm in BCI applications, which involves users imagining specific limb movements without actual muscle activation. EEG data corresponding to these imagined movements were preprocessed and analyzed using the Common Spatial Patterns (CSP) algorithm, a spatial filtering method that enhances class-discriminative features by maximizing variance differences across mental tasks. Subsequently, these features were classified using machine learning techniques implemented in the Python 3.7 environment. The EEG datasets used for training and evaluation were obtained from PhysioNet, a widely recognized repository hosted by the Massachusetts Institute of Technology (MIT). The aim of this work is to support the development of real-time, non-invasive BCI systems, with potential applications ranging from neurorehabilitation to the control of assistive devices such as prosthetics and exoskeletons. Additionally, the results offer insight into the implementation of neural signal processing algorithms on embedded systems, paving the way for the development of brain-controlled microchips and next-generation human–machine interfaces.