Foundation models pre-trained through masked reconstruction on large-scale EEG data have emerged as a promising paradigm for learning generalizable neural representations across diverse brain-computer interface applications. However, a critical yet overlooked challenge is that EEG encoders must learn representations invariant to incomplete observations-when different masked views of the same signal have minimal overlap, existing methods fail to constrain them to a consistent latent subspace, leading to degraded transferability. To address this, we propose DARE-EEG, a self-supervised foundation model that explicitly enforces the mask-invariance property through dual-aligned representation learning during pre-training. Specifically, we introduce mask alignment that constrains representations from multiple masked views of the same EEG sample via contrastive learning, complementing anchor alignment that aligns masked representations to momentum-updated complete features for semantic stability. Additionally, we propose conv-linear-probing, a parameter-efficient strategy that adapts pre-trained representations to heterogeneous electrode configurations and sampling rates through decoupled spectro-spatial projections. Extensive experiments across diverse EEG benchmarks demonstrate that DARE-EEG consistently achieves state-of-the-art in accuracy performance while maintaining relatively low parameter complexity and superior cross-dataset portability compared to existing methods. Furthermore, DARE-EEG contributes to effectively discovering and utilizing the rich potential representations in EEG.

翻译：通过在大型脑电图数据上进行掩码重建预训练的基础模型，已成为跨不同脑机接口应用中学习可泛化神经表示的一种有前景范式。然而，一个关键但被忽视的挑战是：脑电图编码器必须学习对不完整观测不变的表示——当同一信号的不同掩码视图重叠极小时，现有方法无法约束它们进入一致的潜在子空间，从而导致迁移性下降。为解决此问题，我们提出DARE-EEG——一种自监督基础模型，通过在预训练期间进行双对齐表示学习，显式强制实现掩码不变性。具体而言，我们引入掩码对齐，通过对比学习约束来自同一脑电图样本的多个掩码视图的表示，并辅以锚点对齐，将掩码表示与动量更新的完整特征对齐以实现语义稳定性。此外，我们提出卷积线性探测这一参数高效策略，通过解耦的频谱-空间投影将预训练表示适配至异构电极配置和采样率。跨多种脑电图基准的广泛实验表明，与现有方法相比，DARE-EEG在保持相对较低参数复杂度和优越跨数据集可移植性的同时，始终在精度性能上达到最优。此外，DARE-EEG有助于有效发现和利用脑电图中丰富的潜在表示。