DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG
DARE-EEG is a new foundation model designed for mining dual-aligned representations of EEG data. It addresses the challenge of learning invariant representations from incomplete observations through a self-supervised approach. The model demonstrates state-of-the-art accuracy while maintaining low parameter complexity and cross-dataset portability.
- ▪DARE-EEG enforces mask-invariance through dual-aligned representation learning during pre-training.
- ▪The model utilizes contrastive learning to align representations from multiple masked views of the same EEG sample.
- ▪Extensive experiments show that DARE-EEG achieves superior performance across diverse EEG benchmarks.
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Computer Science > Artificial Intelligence arXiv:2605.18298 (cs) [Submitted on 18 May 2026] Title:DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG Authors:Yang Shao, Peiliang Gong, Qun Dai, Daoqiang Zhang View a PDF of the paper titled DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG, by Yang Shao and 3 other authors View PDF HTML (experimental) Abstract: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.
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