通过频段解相关时序集成提升脑机接口手写想象解码精度
Frequency-Decorrelated Temporal Ensembles for EEG--fNIRS Imagined-Handwriting Decoding
- 构建多频段解耦的时序集成模型，利用 EEG 多尺度频率特征增强泛化能力
- 在无测试集适配下实现 0.8076/0.7242/0.7492 的跨被试准确率
- 适合需要高鲁棒性脑机接口解码的研究者与开发者参考
想象书写为非侵入式神经解码提供了时间丰富的范式，但因头皮 EEG 噪声大且个体间书写序列差异显著，跨被试可靠识别仍具挑战。多模态脑机接口大赛提供了同步的 EEG 与 fNIRS 数据，用于四类跨被试手写轨迹分类。本文提出 FRED 系统，将想象书写建模为多秒运动序列，基于三个互补的 EEG 频段视图训练紧凑的多尺度时序网络。每个视图采用三种子模型，跨频段成员误差显著低于同频段副本，形成九成员集成，无需测试集适配或输出约束，公开/私有/总体测试集准确率分别为 0.8076、0.7242、0.7492。提交方案进一步融合了半监督伪标签训练、三个 EEG-Conformer 成员、后验聚合及范式感知解码器。由于每 12 次试验随机化块包含每类各三次，最终预测通过匈牙利匹配算法在已知块配额下获得。在单一后验池上，独立、会话受限和块受限解码分别达到 0.7600、0.7758、0.7952 的总体准确率。完整系统在私有集上达 0.8498/0.7718/0.7952，排名第四。模态审计显示仅用 fNIRS 解码达到随机水平（0.2511），而加入 fNIRS 仅提升 +0.0025。结果表明，频段多样性时序建模与协议匹配的结构化推理是该稀疏电极设置下的性能主因。
原文摘要 · Abstract (English)
Imagined handwriting offers a temporally rich paradigm for non-invasive neural decoding, yet reliable recognition across unseen participants remains difficult because scalp EEG is noisy and internally generated stroke sequences vary across individuals. The Multimodal Brain-Computer Interface Grand Challenge provides synchronized EEG and fNIRS for four-class subject-independent handwriting-trajectory classification. We propose FRED, a task-adapted system that models imagined handwriting as a multi-second motor sequence and trains a compact multi-scale temporal network on three complementary EEG frequency views. With three seeds per view, cross-band members produce substantially less-correlated errors than same-band replicas, yielding a clean nine-member ensemble accuracy of 0.8076/0.7242/0.7492 on the public/private/overall test partitions without test-set adaptation or output constraints. The submitted pipeline further incorporates transductive pseudo-label training, three EEG-Conformer members, posterior aggregation, and a paradigm-aware decoder. Because every 12-trial randomization block contains three instances of each class, the final predictions are obtained by Hungarian assignment under the known block quota. On one fixed posterior pool, independent, session-constrained, and block-constrained decoding achieve 0.7600, 0.7758, and 0.7952 overall accuracy, respectively. The complete system reaches 0.8498/0.7718/0.7952, ranking fourth on the private split. A modality audit finds fNIRS-only decoding at chance (0.2511 overall), while adding fNIRS to EEG changes accuracy by only +0.0025. These results identify frequency-diverse temporal EEG modeling and protocol-matched structured inference as the principal sources of performance in this sparse-montage EEG--fNIRS setting. The source code is available at https://github.com/XiuFan719/EEG-fNIRS-fuse-method-for-MM-challenge.
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