Over the past few decades, researchers have utilized motor imagery (MI)-based electroencephalogram (EEG) signals classification for the development of assistive human-robotic interaction. These EEG signals are crucial in the field of brain-computer interfaces (BCIs) and find various applications in the biomedical domain. BCIs enable the acquisition of brain signals, feature extraction, noise reduction, and generation of control signals. The primary aim of this study was to evaluate and compare the performance of Frequency-domain (FD) features using three distinct classifiers: Fine KNN, Medium KNN, and Coarse KNN. The objective was to differentiate between the right and left hand movements using EEG signals. The EEG dataset used in this research was obtained from twenty healthy human subjects during two separate sessions. To minimize noise, band-pass Butterworth filtering was applied to the acquired data. Here employed with Fine KNN, Medium KNN, and Coarse KNN classifiers. Subsequently, the ten features were combined to form a final feature vector, which yielded the best performance when used with the Fine KNN classifier, achieving an accuracy of 93%. Comparatively, the Medium KNN classifier achieved an accuracy of 92.4%. These findings have significant implications for the design of EEG-based technologies such as wheelchairs and prosthetic limbs. In summary, this study aimed to compare the effectiveness of different FD features and KNN classifiers in discerning between right and left hand movements using EEG signals. The results demonstrated that the combination of ten features, when utilized with the Fine KNN classifier, yielded the highest accuracy rate of 93%. These findings have valuable implications for the development of EEG-based assistive devices, including prosthetic limbs and wheelchairs.