Since the past twenty years, a new type of interface has developed in a spectacular way: the Brain-Computer Interfaces (BCI). These interfaces enable users to control computers solely with their thoughts, generating various forms of brainwave activity. Indeed, BCIs are a new means of communication for people with total or partial paralysis. A key challenge in BCI system design involves automatically interpreting the user’s brain activity patterns. Most BCIs are noninvasive systems that rely on the electroencephalogram (EEG) and use one of three mental tasks, among them motor imagery (MI). This chapter focuses on motor imagery-based BCI systems utilizing EEG signal processing. In conventional BCI systems, the detection and interpretation of brain signals typically depend on machine learning classifiers, which require carefully extracted EEG features for accurate operation. In this study, we specifically investigate motor imagery (MI) classification for left- and right-hand movements using EEG signals. Our novel approach introduces two key feature extraction methods. (1) Wavelet Transform (WT) for time-frequency analysis. (2) Mel-Frequency Cepstral Coefficients (MFCC) for spectral representation. These features serve as input to a Support Vector Machine (SVM) classifier, with a Genetic Algorithm (GA) optimizing feature selection. We rigorously evaluate the system’s classification performance against state-of-the-art benchmarks, demonstrating significant improvements in MI recognition accuracy.