EvoBrain introduces a continual learning method with Neuro-Spectral Task Normalization and Response-Affinity Distillation to enable unified EEG decoding across heterogeneous BCI tasks.
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arXiv preprint arXiv:2409.00101 (2024)
14 Pith papers cite this work. Polarity classification is still indexing.
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OmniEEG-Bench unifies 54 EEG datasets into six task families and benchmarks 10 foundation models, finding that pretraining diversity and model size correlate with better average performance ranks.
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.
NeuroAtlas benchmarks foundation models on 42 EEG datasets and reports that EEG-specific models do not consistently outperform generic time-series models, standard metrics miss clinical utility, and rankings vary by domain.
Generative Visual Grounding creates instance-specific visual proxy images from EEG signals to enhance MLLM understanding of brain activity beyond text-only alignment.
CLEF, a long-context EEG foundation model using 3D multitaper spectrograms and contrastive alignment with reports and EHR, beats prior models on 229 of 234 clinical tasks and raises mean AUROC from 0.65 to 0.74.
EduGage releases a multimodal sensor dataset and models for estimating learner engagement in self-guided video learning, reporting MAE of 0.81 and outperforming baselines with 16 participants.
LLM-based refinement of edges in transformer-constructed EEG graphs improves seizure detection accuracy and produces cleaner, more interpretable structures on the TUSZ dataset.
fMRI-LM builds a foundation model that aligns fMRI signals with language through tokenization, LLM adaptation, and instruction tuning to enable semantic understanding of brain activity.
TFM-Tokenizer learns a vocabulary of time-frequency motifs from single-channel EEG via a dual-path masked architecture and encodes signals into discrete tokens, reporting up to 11% Cohen's Kappa gains on benchmarks and 14% on ear-EEG sleep staging.
MindAU is a dual-stream manifold alignment system that conditions a multimodal diffusion editor on EEG signals to perform fine-grained, identity-preserving facial action unit edits.
Large Sensor Models trained on large-scale multimodal wearable data can provide a scalable, general framework for wearable AI by learning transferable representations across modalities and tasks.
A unified benchmark across 12 ERP datasets finds that foundation models and deep learning generally outperform traditional manual features for stimulus classification and disease detection, with specific embedding strategies improving Transformer performance.
NeuroWeaver reformulates EEG pipeline design as constrained evolutionary optimization with domain-informed initialization, yielding lightweight pipelines that outperform task-specific methods and match foundation models on five benchmarks.
citing papers explorer
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EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks
EvoBrain introduces a continual learning method with Neuro-Spectral Task Normalization and Response-Affinity Distillation to enable unified EEG decoding across heterogeneous BCI tasks.
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OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models
OmniEEG-Bench unifies 54 EEG datasets into six task families and benchmarks 10 foundation models, finding that pretraining diversity and model size correlate with better average performance ranks.
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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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NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces
NeuroAtlas benchmarks foundation models on 42 EEG datasets and reports that EEG-specific models do not consistently outperform generic time-series models, standard metrics miss clinical utility, and rankings vary by domain.
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Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs
Generative Visual Grounding creates instance-specific visual proxy images from EEG signals to enhance MLLM understanding of brain activity beyond text-only alignment.
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CLEF: EEG Foundation Model for Learning Clinical Semantics
CLEF, a long-context EEG foundation model using 3D multitaper spectrograms and contrastive alignment with reports and EHR, beats prior models on 229 of 234 clinical tasks and raises mean AUROC from 0.65 to 0.74.
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EduGage: Methods and Dataset for Sensor-Based Momentary Assessment of Engagement in Self-Guided Video Learning
EduGage releases a multimodal sensor dataset and models for estimating learner engagement in self-guided video learning, reporting MAE of 0.81 and outperforming baselines with 16 participants.
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LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis
LLM-based refinement of edges in transformer-constructed EEG graphs improves seizure detection accuracy and produces cleaner, more interpretable structures on the TUSZ dataset.
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fMRI-LM: Towards a Universal Foundation Model for Language-Aligned fMRI Understanding
fMRI-LM builds a foundation model that aligns fMRI signals with language through tokenization, LLM adaptation, and instruction tuning to enable semantic understanding of brain activity.
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Tokenizing Single-Channel EEG with Time-Frequency Motif Learning
TFM-Tokenizer learns a vocabulary of time-frequency motifs from single-channel EEG via a dual-path masked architecture and encodes signals into discrete tokens, reporting up to 11% Cohen's Kappa gains on benchmarks and 14% on ear-EEG sleep staging.
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MindAU: EEG-Conditioned Facial Action Unit Editing via Dual-Stream Manifold Alignment
MindAU is a dual-stream manifold alignment system that conditions a multimodal diffusion editor on EEG signals to perform fine-grained, identity-preserving facial action unit edits.
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Wearable AI in the Era of Large Sensor Models
Large Sensor Models trained on large-scale multimodal wearable data can provide a scalable, general framework for wearable AI by learning transferable representations across modalities and tasks.
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NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines
NeuroWeaver reformulates EEG pipeline design as constrained evolutionary optimization with domain-informed initialization, yielding lightweight pipelines that outperform task-specific methods and match foundation models on five benchmarks.