AI 中文总结
本研究针对EEG-fNIRS想象手写解码难题，提出FRED系统，采用频率去相关时间集成等方法，在多模态脑机接口挑战赛中取得较好成绩，揭示了关键性能来源。
AI 中文摘要
想象手写为无创神经解码提供了时间丰富的范式，但由于头皮脑电图（EEG）存在噪声，且个体内部生成的笔画序列存在差异，在未见过的被试间实现可靠识别仍存在困难。多模态脑机接口挑战赛提供了同步的EEG和功能性近红外光谱（fNIRS）数据，用于四分类的被试无关手写轨迹分类。我们提出FRED，这是一个任务适配的系统，将想象手写建模为多秒的运动序列，并在三个互补的EEG频率视图上训练一个紧凑的多尺度时间网络。每个视图使用三个随机种子，跨频带成员产生的错误相关性远低于同频带副本，从而在公共/私人/整体测试分区上获得了干净的九成员集成准确率，分别为0.8076、0.7242、0.7492，且无需测试集适配或输出约束。提交的流程进一步包含转导伪标签训练、三个EEG-Conformer成员、后验聚合和范式感知解码器。由于每12次试验的随机化块包含每个类别的三个实例，最终预测通过匈牙利算法在已知块配额下进行分配。在一个固定后验池上，独立、会话约束和块约束解码的整体准确率分别为0.7600、0.7758和0.7952。完整系统达到0.8498/0.7718/0.7952，在私人分区排名第四。模态审计发现仅fNIRS解码处于随机水平（整体准确率0.2511），而将fNIRS添加到EEG中仅使准确率变化+0.0025。这些结果表明，频率多样化的EEG时间建模和协议匹配的结构化推理是该稀疏导联EEG-fNIRS设置中性能的主要来源。源代码可在指定URL获取。
英文摘要
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.
