Channel-wise attention mechanisms have significantly improved deep learning-based decoding in brain-computer interfaces (BCIs). However, these methods often fail to fully utilize spatial and temporal dynamics, focusing instead on individual channel enhancements to the detriment of broader EEG signal dynamics. To address these limitations, we introduce the Enhanced Local Attention (ELA) module, seamlessly integrated into deep neural networks that enhances EEG decoding performance. It captures long-range dependencies from deep features across two dimensions through a streamlined architecture. The ELA module employs adaptive 1D convolution for precise localization of spatial and temporal information without reducing dimensions. Additionally, it utilizes group normalization to enhance feature representation by normalizing features across groups. Its lightweight design enables easy integration into existing deep learning frameworks. Comprehensive evaluations on the BCI-IV2b dataset highlight the ELA module’s superior performance. Specifically, it increases the decoding accuracy of EEGNet in the motor imagery task from 81.89% to 85.97%. Furthermore, when integrated with ADFCNN, the ELA module achieves an accuracy of 88.13%, consistently outperforming other state-of-the-art methods.