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Brain-Computer Interfaces (BCIs) offer promising applications in neurological rehabilitation through motor imagery (MI)-based training, which is the "intent'' of performing an action. This research addresses the challenge of accurately classifying MI and motor execution (ME) based on Electroencephalography (EEG) signals. This kind of data is often limited by subject variability, non-stationarity, environmental noise during data collection, EEG device quality, and small dataset sizes. For our study, we propose to make use of an external large dataset, including data from 103 subjects (compared to the 9–12 subject datasets used in prior work). One of the main goals of this research is to integrate multiple feature extraction techniques spanning time, frequency, and spatial domains. Effective EEG channel selection was guided by fMRI studies identifying MI- and ME-relevant Brodmann areas, combined with EEG-based statistical analysis, resulting in a refined set of 12 informative electrodes. Several machine learning models (SVM, RF, KNN, XGBoost, MLP) are evaluated, achieving up to 80% accuracy with improved robustness across subjects. These findings demonstrate enhanced generalizability and support the development of more reliable BCI applications for real-world rehabilitation scenarios.