Motor imagery (MI)-based brain-computer interfaces (BCIs) require efficient electrode selection to reduce cost and computational complexity while preserving their performance. We propose a signal-to-noise ratio (SNR)-based electrode selection strategy and introduce a novel method to calculate SNR from EEG datasets. We further present the Pairwise Difference (PRD) technique for data augmentation to compensate for the accuracy loss resulting from electrode reduction. Using EEGNet, a state-of-the-art deep learning model, we achieved an accuracy of 68.99% and an F1-score of 0.6891 with all 22 electrodes as the baseline. As electrodes were progressively removed, performance declined, with a notable drop after a 50% reduction. However, applying the PRD technique maintained EEGNet's average accuracy within 3.96% of the baseline (66.26%), and F1-score at 0.6587, even with the above reduction. PRD effectively compensated for the loss of information due to electrode reduction. Simulations further validated the generalizability of our approach, demonstrating its effectiveness across other deep learning models, viz., ShallowConvNet and DeepConvNet. This work will help develop cheaper and resource-efficient BCI systems.