NeuroMambaNet: A Prior-Integrated Dual-Stream Mamba Network for Robust Cross-Subject Motor Imagery EEG Classification

Cross-subject motor-imagery electroencephalography (MI-EEG) classification remains challenging due to substantial inter-subject variability and non-stationarity, which cause task-relevant spectral and spatial patterns to vary across users and reduce the robustness of models trained on source subjects. To address this issue, we propose NeuroMambaNet, a brain-informed dual-stream network that explicitly integrates spectral, spatial, and rhythmic priors. In the frequency-enhanced stream, a Mamba-Augmented Multi-Band Oscillation (MAMBO) module decomposes EEG with a Discrete Wavelet Transform (DWT), models the physiologically relevant θ, α, β, and γ sub-bands using independent Mamba blocks, and reconstructs a band-enhanced signal through inverse DWT (IDWT). In parallel, the raw stream extracts complementary spatial representations using two Brain-Region Prior Grouping (BRDW) branches based on left–central–right and central–outer electrode partitions. β-band evidence from frontal-central electrodes is then used by a lightweight Beta-Gate module to adaptively weight the two spatial branches. The frequency-enhanced and gated raw representations are fused at a later stage and processed by a global temporal convolution for classification. Under a strict leave-one-subject-out (LOSO) protocol, NeuroMambaNet achieves an average accuracy of 59.04% and a Cohen’s κ of 0.4539 on the BCI IV-2a dataset, and 69.20% and 0.3839, respectively, on the CBCIC dataset, outperforming evaluated baseline methods and demonstrating the potential of the proposed architecture for subject-independent MI-EEG classification.