DA2Mel: A Dual-Dynamic Adaptive Network for EEG-to-Mel Spectrogram Reconstruction

Decoding neural activity into natural speech is a frontier in cognitive neuroscience and brain-computer interfaces. Existing methods overlook two critical issues when decoding continuous long-sequence speech: the pronounced inter-subject variability and substantial temporal fluctuations within individual EEG signals, as well as the inherent transverse sparsity present in acoustic features. To address these issues, this paper proposes DA2Mel, a dual-dynamic adaptive network that enhances the perception of non-stationary neural signals. The Dynamic Subject-Specific Memory integrator (DSSM) forms an adaptive closed loop using identity-aware memory and momentum updating. It adapts to EEG distribution shifts from inter- and intra-subject variations, aligns with dynamic physiological states, and ensures long-term robustness. The Dual-Pathway Spatial-Channel Processor (DP-SCP) extracts critical channel information, fuses multi-scale spatial features, and bridges global trends and local details. In addition, the Stacked Fine-Grained Module (SFGM) employs a content-aware adaptive mechanism, fusing local convolutions with partial Top- \\(k\\) sparse attention. By dynamically reconstructing representation weights based on the instantaneous sparsity of acoustic features, SFGM distills pivotal characteristics and optimizes semantics under noise. Experimental results on the SparrKULee dataset demonstrate that DA2Mel outperforms state-of-the-art methods in both the Held-out Stories and Held-out Subjects settings, validating the superiority of our framework for continuous speech decoding.