Accurate classification of electroencephalogram (EEG) motor imagery tasks is critical for advancing brain–computer interface (BCI) applications. This paper proposes a streamlined phase-based approach to distinguish motor imagery tasks by extracting and leveraging phase information inherent in EEG signals. The method involves decomposing EEG data into relevant frequency bands, computing phase features using analytic signal techniques, and applying feature selection to enhance discriminative power. Experimental evaluation on benchmark motor imagery datasets demonstrates that the phase-based features significantly improve classification accuracy compared to traditional amplitude-based methods. The approach is computationally efficient, robust to noise, and adaptable to real-time BCI systems. These findings underscore the potential of phase information as a valuable modality for refining motor imagery recognition and optimizing user performance in neurotechnology applications.