arXiv eess.SP· Ying Zhou, Yaoxin, Mengting Liu·· 3 天前AI 评分34
TriRouter：面向鲁棒 EEG 运动想象解码的稀疏到稠密电极空间路由
TriRouter: Sparse-to-Dense Electrode-Space Routing for Robust EEG Motor Imagery Decoding
AI 导读
研究提出电极空间路由框架 TriRouter，通过判别保持、掩码鲁棒与潜在补偿三条路径，并依据可观测通道可用性由验证校准路由器分配各路径贡献。在 BNCI2014-001 上，其在标称 25%、50%、75% 缺失设置下分别比最强的受损 EEG 基线提升 3.68、4.02、6.46 个百分点，在结构化区域缺失和额外低通道数据集评估中仍有效。
正文 · 原文
Abstract: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 severe missingness. In BNCI2014-001, TriRouter outperforms standard public backbones and a dynamic-spatial-filtering corrupted-EEG baseline. It improves accuracy over the strongest corrupted-EEG baseline by 3.68, 4.02, and 6.46 percentage points at nominal 25%, 50%, and 75% missing settings, and remains effective under structured regional missingness and additional low-channel dataset evaluation.
| Comments: | Submitted to IEEE ICASSP 2027 |
| Subjects: | Machine Learning (cs.LG); Signal Processing (eess.SP) |
| Cite as: | arXiv:2610.23665 [cs.LG] |
| (or arXiv:2610.23665v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.23665 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ying Zhou [view email]
[v1]
Fri, 16 Oct 2026 15:59:00 UTC (1,038 KB)
来源：arXiv eess.SP · arxiv.org