Motor imagery-based brain–computer interfaces (BCIs) have attracted significant attention due to their potential applications in assistive technologies, neurorehabilitation, and wearable human–machine interaction systems. However, practical implementation of EEG-based BCIs remains challenging because high-density EEG recordings increase hardware complexity, prolong setup time, and introduce substantial spatial redundancy. In addition, the performance of motor imagery classification strongly depends on preprocessing strategy and temporal segmentation parameters. Unlike previous studies that primarily focused on developing new deep learning architectures, this work proposes a systematic optimization framework for identifying practical low-channel EEG configurations by jointly analyzing channel selection, temporal segmentation, and preprocessing strategies for subject-independent motor imagery classification. This study investigates the influence of EEG channel reduction, temporal window segmentation, and baseline correction on motor imagery classification performance using EEGNet-based deep learning architectures. Experiments were conducted using the publicly available PhysioNet EEG Motor Movement/Imagery dataset under subject-independent evaluation conditions. Several EEG configurations were analyzed, including full-scale 64-channel recordings and reduced 15-, 6-, 3-, and 2-channel motor-cortex setups. The obtained results demonstrate that reduced-channel EEG configurations can achieve performance comparable to full-scale recordings. The best classification accuracy of 65.04% was achieved using a 15-channel motor configuration combined with 2 s sliding-window segmentation and baseline correction, achieving performance comparable to the conventional 64-channel setup (64.76%), while substantially reducing the number of electrodes and hardware complexity. Statistical analysis confirmed that the difference between the two configurations was not significant (paired t-test, p = 0.1684). Furthermore, compact 3-channel configurations maintained classification accuracy above 60%, supporting the feasibility of lightweight wearable EEG systems for practical BCI applications. The experiments additionally revealed that shorter temporal windows improve classification stability and reduce susceptibility to unrelated background EEG activity. Baseline correction significantly improved model generalization by compensating for inter-trial signal variability and slow EEG drift. Overall, the findings of this study demonstrate that careful optimization of electrode selection and preprocessing strategies can substantially improve the practicality of lightweight EEG-based motor imagery classification systems while reducing hardware complexity and preserving competitive performance.