arXiv Computer Vision
ViTexSZ: Heterogeneous Vision-Text Knowledge Distillation for EEG Seizure Detection
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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.
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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
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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
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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
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arXiv AI
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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...