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.
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Neuroprobe is a new suite of decoding tasks on the BrainTreebank iEEG dataset for evaluating multi-modal language processing in the brain during naturalistic movie viewing.
Brain-OF is a multimodal foundation model for fMRI, EEG and MEG using any-resolution sampling, DINT attention with sparse MoE, and masked temporal-frequency pretraining on ~40 datasets to achieve superior downstream performance.
Microstate tokenizer from clustered EEG signals provides universal representations that outperform traditional time- and frequency-domain features across sleep staging, emotion recognition, and motor imagery tasks.
citing papers explorer
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DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG
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.
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Neuroprobe: Evaluating Intracranial Brain Responses to Naturalistic Stimuli
Neuroprobe is a new suite of decoding tasks on the BrainTreebank iEEG dataset for evaluating multi-modal language processing in the brain during naturalistic movie viewing.
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Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG
Brain-OF is a multimodal foundation model for fMRI, EEG and MEG using any-resolution sampling, DINT attention with sparse MoE, and masked temporal-frequency pretraining on ~40 datasets to achieve superior downstream performance.
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Atoms of Thought: Universal EEG Representation Learning with Microstates
Microstate tokenizer from clustered EEG signals provides universal representations that outperform traditional time- and frequency-domain features across sleep staging, emotion recognition, and motor imagery tasks.
- Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs