Convolutional Neural Networks (CNNs) and Transformer-based models have been extensively explored in brain–computer interface (BCI) systems based on motor imagery (MI) for decoding electroencephalogram (EEG) signals. However, CNNs are limited in modeling long-range dependencies, while the self-attention mechanism of Transformers is constrained by its quadratic computational complexity as input size increases. Recently, State Space Models (SSMs) like Mamba, with hardware-aware designs, have shown strong capability in long-sequence modeling, offering linear computational complexity. Inspired by this, we propose DBAM-EEG, a model designed for efficient decoding of EEG-based MI signals. DBAM-EEG consists of three main modules: attention bidirectional convolution (ABC) block, Vision Mamba (Vim) Encoder, and weighted residual temporal convolutional network (WRTCN). The ABC block encodes the MI-EEG signals into advanced temporal sequence representations, the Vim Encoder focuses on capturing discriminative temporal features within the sequences, and the WRTCN extracts high-level temporal features. On the BCI Competition IV-2a dataset, DBAM-EEG achieved classification accuracies of 87.65% and 71.51% under subject-dependent and subject-independent paradigms, respectively, surpassing the performance of state-of-the-art methods.