A tri-modal contrastive learning method for EEG-based zero-shot visual decoding reports 54.1% top-1 accuracy on the Things-EEG2 200-way benchmark, outperforming prior baselines of 32.4%.
Thd-bar: Topology hierarchical derived brain autoregressive modeling for eeg generic representations.arXiv preprint arXiv:2511.13733, 2025
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SGC uses anomaly scores from an unsupervised generative network as a normalized pathological prior fused into deep features to improve EEG-based MDD detection without data augmentation or synthesis.
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MindAlign: Bridging EEG, Vision, and Language for Zero-Shot Visual Decoding
A tri-modal contrastive learning method for EEG-based zero-shot visual decoding reports 54.1% top-1 accuracy on the Things-EEG2 200-way benchmark, outperforming prior baselines of 32.4%.
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Beyond Augmentation: Score-Guided Pathological Prior for EEG-based Depression Detection
SGC uses anomaly scores from an unsupervised generative network as a normalized pathological prior fused into deep features to improve EEG-based MDD detection without data augmentation or synthesis.