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 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
5d ago
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
Sep 4
RobustSeiz: An Open-Source Framework for Benchmarking the Robustness of EEG Seizure Detection Models
RobustSeiz is an open‑source, model‑agnostic framework designed to benchmark the robustness of EEG seizure detection models under realistic clinical stressors. It standardizes four public scalp‑EEG corpora into BIDS‑EEG trees, applies controlled distribution shifts—including environmental, noise, and adversarial transforms—across predefined hyperparameter grids, and reports comprehensive performance metrics such as sensitivity, precision, F1, false positives per 24 h, onset timing, and predictive agreement. The framework offers a Dockerized GPU pipeline, experiment registry, and both full‑evaluation and research‑subset modes, and demonstrates its utility by evaluating a contemporary detector on TUSZ across the full shift grid.
arXiv AI
Sep 1
A foundation model with multi-variate parallel attention to generate neuronal activity
arXiv:2506.20354v3 Announce Type: replace-cross Abstract: Learning from multi-variate time-series with heterogeneous channel configurations remains a fundamental challenge for deep neural networks, p...
arXiv Machine Learning
1d ago
Attention-Enhanced Dual-Branch ConvNeXt-BiLSTM Network for Subject-Independent EEG Seizure Detection
arXiv:2609.22141v1 Announce Type: cross Abstract: Automated seizure detection from scalp electroencephalography (EEG) is difficult because seizure morphology varies among patients and seizure samples...
arXiv AI
Aug 6
BrainBench: Benchmarking Large Language Models for Comprehensive EEG Understanding
arXiv:2608. 04156v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis extends beyond assigning predefined labels to recordings; it requires workflows connecting natural-language instructions, signal processing, quantitative evidence, and scientific interpretation.
arXiv AI
Jul 29
Joint Text-Audio Alignment for EEG-to-Text Decoding in Chinese Speech Production and Perception
arXiv:2607. 25626v1 Announce Type: new Abstract: Decoding speech information directly from scalp electroencephalography (EEG) into text provides a potential non-invasive neural communication pathway for individuals with severe speech and motor impairments.
arXiv Machine Learning
Jul 22
Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark
arXiv:2607. 18749v1 Announce Type: new Abstract: Translating brain signals into text could restore communication for people with severe paralysis, yet practically usable systems to date rely on invasive electrocorticography (ECoG).