NeuroCodeX: Beyond Homogenization in EEG Joint Pretraining

Learning effective EEG representations remains challenging when downstream tasks have limited labeled data. Joint pretraining on diverse large-scale EEG datasets offers a promising solution, but substantial differences in electrode montages, channel configurations, and sampling rates make it difficult to directly combine datasets. Existing approaches typically address these discrepancies by enforcing a unified input structure through signal resampling, channel padding, masking, or fixed channel selection, thereby suppressing dataset-specific structural characteristics and limiting the ability of joint pretraining to exploit complementary information across datasets. In this work, we propose NeuroCodeX, a structure-aware framework that moves beyond homogenization by explicitly modeling structural differences during EEG joint pretraining. NeuroCodeX consists of two components: a Hierarchical Topographic Encoder that captures anatomy-aware spatial dependencies through hierarchical region-level aggregation, and a Multi-Scale Quantizer that provides rhythm-aware discrete supervision across different sampling rates. We evaluate NeuroCodeX on four EEG datasets spanning diverse acquisition settings, channel configurations, and downstream tasks. Experimental results demonstrate consistent improvements over state-of-the-art baselines, with particularly strong benefits under data-scarce conditions. These findings suggest that effective EEG joint pretraining depends not simply on combining more data, but on appropriately preserving and modeling the structural characteristics of individual datasets. Code is available at: https://anonymous.4open.science/r/NeuroCodeX.