解决脑电图模型低频偏差问题，提升跨任务泛化能力
Understanding and Correcting Low-Frequency Bias in EEG Foundation Model
- 设计频率均衡的掩码自编码框架，按频段独立标准化重建目标
- 在OmniEEG-Bench 41项任务中24项达顶尖水平，显著改善频谱平衡性
- 适合关注脑电信号表征质量与跨任务迁移的研究者
扩大脑电图预训练数据规模或模型容量，并不能持续提升下游性能。我们发现多种脑电图基础模型存在持续的低频偏差，该偏差在不同数据集规模、模型容量和预训练目标下均存在。分析表明，这种偏差源于脑电信号的$1/f^α$类频谱结构与神经网络对低频成分的偏好之间的相互作用。在掩码自编码器中，$\ ell_2$重构目标进一步加剧了不平衡：在相对重构误差相近的情况下，高功率低频成分对损失贡献更大。为此，我们提出FAME，一种频率均衡的掩码自编码框架，从掩码输入中重构预定义脑电频段的时间-频率活动。FAME在每个频段内独立标准化重构目标，并对各频段损失赋予相等权重，从而实现全频谱监督平衡。在OmniEEG-Bench的41个下游任务上评估，FAME学习到更频谱均衡的表示，在24项任务中达到最先进性能。结果凸显了平衡频谱监督对学习可迁移脑电表示的重要性。
原文摘要 · Abstract (English)
Increasing EEG pretraining data scale or model capacity does not consistently improve downstream performance. We identify a persistent low-frequency bias in representations learned by diverse EEG foundation models, which remains across dataset scales, model capacities, and pretraining objectives. Our analysis links this bias to the interaction between EEG's $1/f^α$-like spectral structure and neural networks' tendency to preferentially learn low-frequency components. In masked autoencoders, the $\ell_2$ reconstruction objective further amplifies this imbalance: under comparable relative reconstruction errors, high-power low-frequency components contribute disproportionately to the loss. To address this issue, we introduce FAME, a frequency-balanced masked autoencoding framework that reconstructs time--frequency activity in predefined EEG bands from masked EEG inputs. FAME independently standardizes the reconstruction targets within each band and assigns equal weight to all band-specific losses, thereby balancing supervision across the EEG spectrum. Evaluated on 41 downstream tasks in OmniEEG-Bench, FAME learns more spectrally balanced representations and achieves state-of-the-art performance on 24 of them. These results underscore the importance of balanced spectral supervision for learning transferable EEG representations.
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