JET is a conditional flow matching framework that generates EEG as continuous raw sequences with added constraints for spectral and temporal properties, achieving over 40% lower TS-FID than prior discrete denoising methods on three benchmarks.
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BLEG enhances GNNs for fMRI brain network analysis by prompting LLMs for text augmentation, using cost-effective instruction tuning, and applying alignment losses during joint training.
OmniMouse demonstrates data-driven scaling in multi-task brain models on a 150B-token neural dataset, achieving SOTA across prediction, decoding, and forecasting while model size gains saturate.
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.
citing papers explorer
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Let EEG Models Learn EEG
JET is a conditional flow matching framework that generates EEG as continuous raw sequences with added constraints for spectral and temporal properties, achieving over 40% lower TS-FID than prior discrete denoising methods on three benchmarks.
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BLEG: LLM Functions as Powerful fMRI Graph-Enhancer for Brain Network Analysis
BLEG enhances GNNs for fMRI brain network analysis by prompting LLMs for text augmentation, using cost-effective instruction tuning, and applying alignment losses during joint training.
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OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
OmniMouse demonstrates data-driven scaling in multi-task brain models on a 150B-token neural dataset, achieving SOTA across prediction, decoding, and forecasting while model size gains saturate.
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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.
- Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs