发表机构
Beijing Institute of Technology（北京理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有脑电生成方法忽略脑电时刻异质性的问题，提出基于条件流匹配的自适应框架，引入位置自适应时间调度等模块，在三类脑电数据集上优于基线，提升生成质量与下游任务性能。
AI 中文摘要
脑电（EEG）生成对于缓解脑机接口应用中的数据稀缺问题、实现大规模神经建模至关重要。然而，现有的基于流（flow）的方法假设样本内的每个通道和每个时间片段共享单一全局时间进程，忽略了并非所有脑电时刻都平等这一异质性。为解决这一被忽视的异质性，我们提出了一种基于条件流匹配（conditional flow matching）的自适应脑电生成框架。该框架引入位置自适应时间调度，通过跟踪每个位置的重构误差来调节流匹配轨迹内的位置特异性时间进程；还整合了因子化时空注意力（Factorized Spatio-Temporal Attention）和频率对齐的多分辨率谱一致性损失，以建模容积传导诱导的通道间依赖关系，并补偿脑电的幂律谱偏差，从而提升生成信号的质量。在三个具有不同采集协议和任务语义的脑电数据集上开展的大量实验表明，我们的框架始终优于最强基线，将TS-FID降低了多达62.2%，并将下游分类准确率增益提升了多达6.77个百分点。这些结果表明，所提出的方法为现实世界脑机接口应用中可扩展、高保真的数据增强迈出了有前景的一步。
英文摘要
Electroencephalography (EEG) generation is essential for alleviating data scarcity and enabling large scale neural modeling in brain computer interface applications. However, existing flow based approaches assume that every channel and every time segment within a sample shares a single global time progression, overlooking the fact that not all EEG moments are equal. To address this overlooked heterogeneity, we propose an adaptive EEG generation framework built on conditional flow matching. The framework introduces Position-Adaptive Time Scheduling, which tracks per position reconstruction error to modulate a position specific time progress within the flow matching trajectory. It further incorporates Factorized Spatio-Temporal Attention and a frequency aligned multi resolution spectral consistency loss to model inter channel dependencies induced by volume conduction and compensate for the power law spectral bias of EEG, thereby improving the quality of generated signals. Extensive experiments on three EEG datasets with distinct acquisition protocols and task semantics show that our framework consistently outperforms the strongest baseline, reducing TS-FID by up to 62.2\% and improving downstream classification accuracy gain by up to 6.77 percentage points. These results suggest that the proposed method represents a promising step toward scalable, high fidelity data augmentation for real world brain computer interface applications.
