TCANet is a novel end-to-end model designed for motor imagery EEG signal decoding, enhancing the capabilities of existing frameworks like CTNet and MSCFormer. It employs a combination of multi-scale CNN, temporal convolutional networks, and multi-head self-attention to effectively capture spatiotemporal dependencies, achieving high classification accuracies on BCI IV-2a and IV-2b datasets. The model demonstrates competitive performance in both subject-dependent and subject-independent settings, indicating its potential for advancing brain-computer interface systems.