Motor imagery-based brain-computer interface (MI-BCI) faces a critical challenge in achieving effective spatialtemporal feature modeling while maintaining a compact model parameterization. Herein, a lightweight model was proposed, termed as Dual-Attention-EEGNet (DA-EEGNet), which extends the EEGNet backbone by integrating a channel attention module and a depth attention module to selectively emphasize informative electrodes and temporally discriminative features. Two widely used MI benchmark datasets and three evaluation strategies, i.e., subject-dependent scenario, subject-independent scenario, and dataset-independent classification scenario, were utilized to verify the model’s performance. Despite its compact design, DA-EEGNet contains merely 3.97k trainable parameters and achieves average classification accuracies of 79.12 ± 13.09% and 86.12 ± 11.92%, outperforming or matching existing deep learning approaches that rely on substantially larger parameter counts. Ablation studies further confirm the complementary contributions of the channel and depth attention modules. In addition, visualization analyses, including temporal attention heatmaps and motor-area topographies, demonstrate that DA-EEGNet captures neurophysiologically meaningful spatial-temporal patterns consistent with MI-related brain activity. These results indicate that DA-EEGNet provides a favorable parameter-accuracy trade-off and serves as an efficient and interpretable baseline for MI-BCI applications.
Wang et al. (2026) studied this question.