NMCNet: A Cortex-Guided EEG-EMG Fusion Framework for Cross-Subject Decoding of Mandarin Initials and Finals

Brain-computer interface (BCI) speech decoding offers a promising communication pathway for individuals with severe speech impairments. However, Mandarin initial and final decoding remains underexplored, and existing EEG-EMG fusion methods rarely encode an asymmetric interaction motivated by cortical control and articulatory muscle responses. We propose NMCNet, a cross-subject EEG-EMG fusion framework for audible and silent speech decoding. NMCNet integrates Cortical Drive Gating, which uses EEG features to modulate EMG representations, with Feedback Cross-Modal Fusion, which reintegrates the resulting muscle-response information into the EEG representation. A complementary multi-frequency branch captures frequency-specific dynamics. To improve cross-subject generalization, we further introduce a cross-subject-enhanced supervised contrastive loss that emphasizes same-class samples from different subjects. A Fisher-based channel-selection strategy reduces the EEG input to 15 channels while outperforming the full channel configuration, supporting lightweight acquisition. Across ten participants, NMCNet achieves the best performance under silent speech, with a mean accuracy of 70.25% across the initial and final tasks, each comprising three broad articulatory categories. In a fine-grained setting that treats each individual initial or final within its broad category as a separate class, NMCNet achieves macro-averaged F1 and recall scores of 73.48% and 74.35%, respectively. Ablation studies support the effectiveness of the proposed architectural components and cross-subject learning strategy. These results demonstrate the potential of lightweight cross-subject Mandarin decoding and silent-speech BCIs for individuals with anarthria, severe dysarthria, and other severe motor speech impairments.