MExFormer: Rethinking Early Channel Fusion for Multichannel EEG Representation Learning

Learning informative EEG representations for identifying neurological states requires capturing channel-specific temporal dynamics and dependencies across spatially distinct electrodes. Existing multivariate time-series Transformers employ diverse strategies for channel interaction, including mixing information across channels during tokenization or embedding rather than retaining individual channels as explicit attention tokens, potentially limiting the direct modeling of inter-electrode interactions. We therefore introduce MExFormer, a Transformer that independently tokenizes each channel into temporally localized patches at multiple resolutions and defers channel fusion to explicit inter-channel self-attention, progressively integrating channel interactions, temporal structure, and multi-resolution information through a three-stage hierarchy. We evaluate the proposed approach on EEG classification across two public datasets under a subject-independent protocol. Beyond classification performance, we assess the quality of learned representations through robustness to corrupted EEG inputs and post-hoc evaluation of intermediate-layer representations using a weak classifier. Across the datasets and six evaluation metrics, MExFormer achieves the strongest overall performance on APAVA dataset and remains competitive on TDBRAIN dataset, while exhibiting greater robustness to channel corruption and more discriminative intermediate representations.