{"total":10,"items":[{"citing_arxiv_id":"2605.18298","ref_index":37,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG","primary_cat":"cs.AI","submitted_at":"2026-05-18T12:18:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"DARE-EEG is a self-supervised EEG foundation model that enforces mask-invariance via contrastive mask alignment and momentum anchor alignment, plus conv-linear-probing for heterogeneous setups, achieving SOTA accuracy and cross-dataset portability.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18172","ref_index":40,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in 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