直接在原始脑电信号上训练，无需分块或编码，提升模型泛化能力。
B[FM]$^2$: Brain Foundation Model via Flow Matching with SplitUNet
![论文配图：B[FM]$^2$: Brain Foundation Model via Flow Matching with SplitUNet](https://arxiv.org/html/2606.20812v1/overview.png)
- 用连续时间流匹配直接建模原始多通道脑电波，避免离散化损失细节。
- 在9个任务中7个达新最优，仅需约307小时数据（比之前少30倍）。
- 生成的合成脑电图专家难辨真假，适合临床与脑机接口研究者。
EEG基础模型可从大规模脑电数据中学习通用表征，实现单一主干网络在多种临床和脑机接口任务间的迁移。现有模型通常将连续多通道脑电信号离散化为补丁或代码本词元，并使用掩码自监督训练变压器。我们发现这种离散化会破坏连续脑节律并掩盖精细时序动态。为此提出B[FM]$^2$（基于流匹配的脑基础模型），其归纳偏置与数据一致：在原始信号上直接进行连续时间流匹配预训练，无需补丁、分词或掩码。然而，多通道脑电信号对流匹配带来架构挑战：时间维度密集采样且高度自相关（数千时间点），而电极轴较短（数十通道）且分布于不同头皮位置。为解决这一时空不对称性，我们引入SplitUNet，一种将每个模块分解为独立的一维时间与一维电极卷积的速率网络，仅沿时间轴下采样，保留电极拓扑结构在整个层级中的完整性。B[FM]$^2$在9个标准下游脑电分类任务中达到7项新最佳性能，预训练仅需36,895段信号（约307小时），较现有模型减少1-2个数量级（约30倍）。此外，其生成的合成脑电信号经两名持证神经科医生评估，无法与真实脑数据区分（Cohen's $κ = -0.096$）。
原文摘要 · Abstract (English)
EEG foundation models can learn generalizable representations from large-scale EEG corpora to enable single-backbone transfer across diverse clinical and brain-computer interface tasks. Existing models typically discretize the continuous multi-channel EEG waveform into patches or codebook tokens and train a transformer with masked self-supervision. Recognizing that this discretization fragments continuous brain rhythms and obscures fine-grained temporal dynamics, we present B[FM]$^2$(Brain Foundation Model via Flow Matching), whose inductive bias aligns with the data by pretraining directly on the raw signal using continuous-time flow matching without patches, tokenization, or masking. However, multi-channel EEG signals pose an architectural challenge for flow matching: time is densely sampled and highly autocorrelated (thousands of timepoints), while the electrode axis is short (tens of channels) at distinct scalp positions. To address this time-electrode asymmetry, we introduce SplitUNet, a velocity network that factorizes each block into separate 1D temporal and 1D electrode convolutions and downsamples only along time, preserving electrode topology throughout the hierarchy. B[FM]$^2$ sets a new state of the art on 7 of 9 standard downstream EEG classification tasks, using a pretraining budget of only 36,895 segments ($\approx$ 307h), 1-2 orders of magnitude ($\approx$ 30x) less than required by existing EEG foundation models. Further, it generates synthetic EEGs that two board-certified neurologists cannot distinguish from brain data (Cohen's $κ=$ -0.096). https://jd730.github.io/projects/BFM2
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