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.
Transformer-based spatial-temporal feature learning for eeg decoding
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Generative Visual Grounding creates visual proxy images from EEG to enhance MLLM understanding of brain signals beyond text-only alignment.
PRiSE-EEG is a prior-guided EEG foundation model that allocates shared and specialized experts across depth using CKA-derived sigmoid mappings and reports strong cross-paradigm results on 12 benchmarks.
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.
UniMind unifies multi-task brain decoding from EEG by bridging signals to LLMs via a Neuro-Language Connector and dynamic task queries, outperforming prior models by 12% on average across ten datasets.
CodeBrain introduces a decoupled TFDual-Tokenizer and multi-scale EEGSSM architecture for an EEG foundation model pretrained on a large corpus, claiming strong generalization across eight downstream tasks and ten datasets.
MTEEG uses task-specific LoRA modules to jointly adapt a pre-trained EEG model across multiple tasks, outperforming single-task baselines on most metrics in evaluations on six downstream tasks.
TGSN reports 97.78% accuracy on AD/FTD classification and RMSE of 1.93 for MMSE prediction on the XY02 EEG dataset, outperforming baselines by large margins.
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UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding
UniMind unifies multi-task brain decoding from EEG by bridging signals to LLMs via a Neuro-Language Connector and dynamic task queries, outperforming prior models by 12% on average across ten datasets.