arXiv AI
Jul 24
Multimodal Pretraining for Generalizable EEG Representation Learning
arXiv:2607. 21384v1 Announce Type: new Abstract: Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks.
ViTexSZ: Heterogeneous Vision-Text Knowledge Distillation for EEG Seizure Detection
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arXiv Machine Learning
Jul 17
NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis
arXiv:2607. 14314v1 Announce Type: new Abstract: Seizure diagnosis from EEG signals is a critical yet persistently challenging task, due to the complicated neural dynamics and the spurious connections in inter-channel modeling.
arXiv AI
Aug 28
EEG-to-Report: An Annotation and Feature-Text Framework for Training Language Models on Clinical EEG
EEG-to-Report is a browser-based annotation and feature‑text framework that links routine EEG review with the creation of AI‑ready datasets. It ingests multi‑format EEG data, standardizes channels, and provides an interactive viewer with a multimodal annotation layer that combines typed text and transcribed voice notes. For each annotated segment, a feature extraction engine computes standardized spectral, temporal, entropy, Hjorth, connectivity, and spike‑related descriptors, stored alongside clinical descriptions in a portable JSON schema, producing aligned feature‑text pairs for training multimodal EEG‑language models. The framework also includes an auto‑report module that uses an ensemble of convolutional networks and a large language model to draft clinical narratives for neurologist review, thereby streamlining annotation workflows and enabling editable draft reports.
arXiv AI
Sep 30
Spatiotemporal Hyperedges for EEG Seizure Detection and Prediction
The paper introduces HyBrain, a model that uses spatiotemporal hyperedges to capture coordinated EEG seizure activity more efficiently than pairwise edge approaches. By generating one token per channel per second and pooling them into shared hyperedge embeddings, HyBrain performs well across detection and prediction tasks on TUSZ and CHB‑MIT datasets, achieving top AUROC scores and competitive training resources. A qualitative example shows a single hyperedge tracking the preictal‑ictal‑postictal progression of a real seizure.
arXiv AI
Oct 27
CAMEL-CLIP: Channel-aware Multimodal Electroencephalography-text Alignment for Generalizable Brain Foundation Models
arXiv:2603.13272v2 Announce Type: replace-cross Abstract: Electroencephalography (EEG) foundation models have shown promise for learning generalizable representations, yet they remain sensitive to ch...
arXiv AI
Oct 15
Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals
arXiv:2610.16596v2 Announce Type: cross Abstract: Seizure prediction and detection, essential for clinical treatment of epilepsy, remain the crucial task of this neurological disease. EEG and iEEG ar...
arXiv Machine Learning
Oct 27
U-NeXt: General-purpose event detection for neonatal EEG
arXiv:2610.32811v1 Announce Type: new Abstract: We developed U-NeXt, a general framework for neonatal electroencephalography (EEG) event detection based on semantic segmentation. Its convolutional en...
arXiv AI
Sep 7
ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding
ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding introduces a model‑agnostic framework that adaptively aligns EEG signals with visual semantics. It replaces fixed visual or textual anchors with EEG‑aware class‑level contrastive supervision and employs structure‑consistent interpolation to preserve channel‑wise and temporal importance. Across multiple evaluation settings—including subject‑dependent, subject‑independent, strict cross‑subject transfer, and continual adaptation—ProCA delivers significant performance gains, achieving relative Top‑1 improvements ranging from 7.4% to 28.1%.
arXiv Machine Learning
Aug 13
Continuous-Latent Predictive Modeling with Semantic Alignment for EEG-Language Foundation Models
arXiv:2608. 11656v1 Announce Type: new Abstract: Recent advances in EEG foundation models have demonstrated the potential of large-scale pretraining to enable generalizable neural decoding across subjects, recording environments, and datasets.
arXiv Machine Learning
Sep 18
Beyond Flattened Tokens: Structure-Preserving EEG Decoding with Reusable TriDim Blocks
The paper introduces TriDim, a reusable block that preserves the three EEG axes—channel, short‑term temporal, and long‑term temporal—by applying feed‑forward transformations and cross‑axis attention. Stacking these blocks yields TriDimEEG, a standalone EEG decoder that outperforms fifteen other models on eight datasets, achieving a 4.3% relative accuracy gain. Replacing Transformer blocks in existing EEG foundation models with TriDim blocks improves downstream accuracy by 7.4% on average while reducing parameters by 17.0% to 47.3%.
arXiv Machine Learning
3d ago
Recasting Visual Language Models as EEG Foundation Models for Clinical Neurodiagnostics
arXiv:2611.05538v1 Announce Type: new Abstract: Historically automated electroencephalography (EEG) systems was done using task-specific supervised models trained on purposely annotated data. A more...
arXiv Computation and Language
4d ago
EEG-HealthLLM: Hierarchical Adaptation of Large Language Models for Unified Heterogeneous EEG Health Assessment
EEG-HealthLLM introduces a hierarchical adaptation framework that unifies heterogeneous EEG health assessments using large language models. Task‑specific EEG adapters preserve unique spatio‑temporal dynamics, while a shared LoRA module in the LLM backbone captures common high‑level assessment semantics. The new EEG‑HealthBench benchmark reorganizes five EEG datasets into five health‑assessment tasks, and experiments show EEG‑HealthLLM achieves the highest average balanced accuracy and macro‑F1, improving performance while reducing adapter size by 80%.