This study performed by Jinzhou Wu and colleagues proposes a superior motor-imagery knowledge distillation (MIKD) framework consisting of two major modules: a teacher assistant module and a student feedback module. It is known that deep learning algorithms are typically large and more difficult to work with despite its pristine accuracy and complex capabilities. However, smaller models, which are more efficient, fail to achieve the performance of larger models. The use of knowledge distillation (KD) emerges as a possible solution for this problem. This method involves preserving critical features from larger, teacher modules to smaller, student modules. Though advancements have been made using KD for EEG processing, this study explores its effectiveness in motor imaging (MI) decoding due to the complex demands of these non-typical EEG signals. The MIKD’s efficacy was evaluated using three public EEG datasets, with each providing information under different conditions, including various devices, subjects, and sample sizes. The result of this study showed that the proposed framework consistently improved the student module’s performance across the three datasets. However, the main tradeoff was, as expected, that this framework took 3.75 times longer than to train the teaching assistant model alone.