Electroencephalogram (EEG) signals suffer from low signal-to-noise ratio (SNR) and high inter-subject variability, making generalization challenging. While Transformer-based self-supervised methods have advanced EEG representation learning, their quadratic complexity limits efficiency in long-range modeling. We propose EEGM2, a self-supervised framework with a U-shaped encoder-decoder integrated with Mamba-2, achieving linear complexity, reduced memory usage, and faster inference. Moreover, the selective information propagation mechanism of Mamba-2 enables the model to effectively capture long-range dependencies, and a joint L1-spectral loss preserves both temporal dynamics and spectral characteristics. Experiments show that EEGM2 achieves state-of-the-art performance in short- and long-sequence modeling, with strong cross-subject generalization and cross-domain transferability. EEGM2 thus provides an efficient, scalable solution suitable for resource-constrained BCI devices. Models and code are available at: https://github.com/Emotiv/EEGM2-ICDM2025