This paper proposes a method for processing motor imagery-based Electroencephalography (EEG) signals to generate precise signals for Brain-Computer Interface (BCI) devices used in rehabilitation and physical treatments. BCI research is mainly used in neuroprosthetic applications to help improve disabilities. We analyze EEG data from seven healthy individuals using 59-channel caps. The signals are down-sampled to 100 Hz after pre-processing to remove artifacts and noise by using Filter Bank Common Spatial Patterns (FBCSP). EEG features are extracted using the Fisher Discriminant Ratio (FDR). A comprehensive comparison of classification methods is conducted, encompassing statistical techniques, machine learning algorithms, and neural network-based models. Specifically, Linear Discriminant Analysis (LDA) and K-Nearest Neighbors (KNN) are evaluated as statistical classifiers; Support Vector Machine (SVM) is used for the machine learning approach; and Radial Basis Function (RBF), Probabilistic Neural Network (PNN), and Extreme Learning Machine (ELM) are explored as neural network models. Model performance is validated using K-fold cross-validation and confusion matrix analysis. Among all evaluated classifiers, the ELM model—implemented as a single-layer neural network—demonstrates superior classification accuracy, suggesting its strong potential for real-time BCI applications in neurorehabilitation.