Sample-Adaptive Expert Routing for EEG Visual Decoding

EEG visual decoding analyzes the relationship between EEG and visual stimuli by aligning EEG to image. Because cortical responses are biased toward coarse, low-spatial-frequency content, recent work aligns EEG to blurred images that carry more of the visual information reflected in the EEG, but fixes the blur level and representation depth to a single global setting shared across all samples. This ignores the variability of EEG responses across subjects, stimuli, and samples, and hence the ideal degree of blur and level of abstraction is inherently sample-dependent. In this paper, we propose a novel sample-adaptive visual decoding method that composes the alignment target by conditioning on each EEG embedding. Building on a mixture-of-experts formulation, dynamic routers select, per sample, the blur level, encoder layer, and backbone of the visual representation, while two auxiliary objectives ground each expert in its own visual content and supervise the routers with the similarity between the EEG and each expert, respectively. Evaluated by zero-shot retrieval on two benchmarks, the proposed method achieves state-of-the-art performance, and our ablations confirm that the conditional routing and the two auxiliary objectives each contribute to the gains.