An effective electrode selection strategy is crucial in motor imagery (MI)-based brain-computer interfaces (BCIs) to maintain competitive performance while reducing the number of electrodes and overall computational complexity. This study introduces a novel electrode selection method based on signal power and evaluates its impact on MI task classification using the BCI Competition IV-2a dataset. Electrodes were systematically reduced by selecting those with the highest signal power. The proposed method was evaluated using state-of-the-art deep learning models, EEGNet, ShallowConvNet, and DeepConvNet, with classification accuracy and F1-score as performance metrics. EEGNet, with all electrodes, achieved an average accuracy of 69.30 and an average F1-score of 0.6910. As the number of electrodes was progressively reduced, performance declined gradually, with a noticeable drop observed after a 50 reduction. Notably, even with 50 fewer electrodes, accuracy remained within 8.59 of the full-electrode configuration. Topographic analysis showed that electrodes near the motor cortex, exhibiting higher signal power, were most critical for classification. In contrast, peripheral electrodes with lower signal power were less informative and could be removed, demonstrating the effectiveness of the proposed method. Similar trends were observed for ShallowConvNet and DeepConvNet, further confirming the method's generalizability. This approach provides a promising direction for developing more practical, faster, cost-effective, and resource-efficient BCI systems. © 2017 IEEE.