Stroke is recognized as a source of numerous impairments, encompassing deficits in physical, motor, and emotional functions in affected individuals. While the visible manifestations of a stroke are evident, the internal effects on the brain remain mostly enigmatic. Research has shown that utilizing motor imagery tasks via Electroencephalogram (EEG) bio signals achieves a 10% increase in accuracy relative to traditional techniques. This research work aims to employ feature extraction techniques on motor imaging tasks combining right-and left-hand grasping, utilizing motor imagery-based EEG data to extract the most pertinent features from two distinct datasets. One dataset comprises individuals with stroke, and the other consists of healthy individuals. Techniques such as the Common Spatial Filter (CSP) and the Filter Bank Common Spatial Filter (FBCSP) are employed to extract relevant features from the processed and filtered data. Three supervised machine learning algorithms, including Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Gaussian Naïve Bayes (GNB), have been employed for data classification. A comparative study has been conducted to understand the fundamental differences in the EEG signals between stroke patients and healthy individuals. The findings indicated that the FBCSP approach surpassed CSP in both categories of patients, with the SVM achieving an accuracy of up to 98.86% in classifying motor imagery tasks. This comparative study enhances our understanding of Brain-Computer Interface (BCI) systems and motor rehabilitation methods by elucidating critical differences between EEG data from stroke patients and healthy individuals.