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arXiv Machine Learning By Ying Zhou, Yaoxin, Mengting Liu

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

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TriRouter is a new electrode‑space routing framework designed to improve motor‑imagery brain‑computer interface decoding from multichannel EEG when electrode montages are sparse or corrupted. It incorporates discriminative‑preservation, mask‑robustness, and latent‑compensation paths, and uses a validation‑calibrated router that allocates path contributions based on observable channel availability. In the BNCI2014‑001 dataset, TriRouter outperforms standard public backbones and a dynamic‑spatial‑filtering corrupted‑EEG baseline, achieving accuracy gains of 3.68, 4.02, and 6.46 percentage points at 25%, 50%, and 75% missing settings, and remains effective under structured regional missingness and low‑channel evaluations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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