EvoBrain introduces a continual learning method with Neuro-Spectral Task Normalization and Response-Affinity Distillation to enable unified EEG decoding across heterogeneous BCI tasks.
EEGFormer: Towards transferable and interpretable large-scale EEG foundation model
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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.
A cross-modal masked autoencoder creates reusable biosignal fingerprints that match or exceed specialist models on seven cardiovascular tasks using only single-modality input.
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.
PRISM-CTG is the first large-scale foundation model for cardiotocography that uses multi-view self-supervised learning on unlabeled data to learn transferable representations, outperforming baselines on seven downstream tasks with external validation.
BrainJanus presents a unified autoregressive model with a brain tokenizer that maps between neural activity, vision, and language for encoding and decoding tasks.
NeuroSonic introduces a conditional flow-matching framework that learns a deterministic transport from noise to speech conditioned on EEG, reporting up to 26.3% gains in perceptual quality over GAN, diffusion, and mean-flow baselines on cross-subject CineBrain and EAV evaluations.
EEGM2 is a Mamba-2 integrated self-supervised model for EEG that claims linear complexity and state-of-the-art performance on long-sequence modeling and classification tasks.
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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.