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TriRouter: Sparse-to-Dense Electrode-Space Routing for Robust EEG Motor Imagery Decoding

L5 · ResearcherResearcharXiv· 10/16/2026

Presents cutting-edge neural architecture for robust brain-computer interfaces with rigorous evaluation against benchmarks.

AI Summary

Researchers propose TriRouter, a novel electrode-space routing framework that improves EEG motor imagery decoding accuracy by 3.68-6.46 percentage points under severe electrode missingness conditions.

Excerpt

Motor imagery brain-computer interfaces require reliable decoding from multichannel EEG, but practical acquisition often yields sparse or corrupted electrode montages. We propose TriRouter, an electrode-space routing framework with discriminative-preservation, mask-robustness, and latent-compensation paths. A validation-calibrated router uses observable channel availability to allocate path contributions, preserving complete-channel discrimination while increasing representation recovery under s

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