{"slug": "eeg-to-report-an-annotation-and-feature-text-framework-for-training-language-on", "title": "EEG-to-Report: An Annotation and Feature-Text Framework for Training Language Models on Clinical EEG", "summary": "Researchers introduced EEG-to-Report, a browser-based framework that links routine EEG review with AI-ready dataset construction, integrating multi-format EEG ingestion, channel standardization, and a multimodal annotation layer with typed text and voice notes. The framework computes standardized spectral, temporal, entropy, Hjorth, connectivity, and spike-related descriptors stored in a portable JSON schema, and includes an auto-report module that couples convolutional networks with a large language model to draft clinical narratives for neurologist review. This provides a reusable foundation for automated EEG reporting systems.", "body_md": "arXiv:2608.26153v1 Announce Type: new\nAbstract: Clinical electroencephalography (EEG) reporting remains largely manual and time-consuming, and current EEG software ecosystems do not produce the structured EEG-text supervision needed for training modern language models. Most toolboxes focus on visualization or preprocessing, providing limited support for workflows that generate high-quality datasets for AI. We introduce EEG-to-Report, a browser-based annotation and feature-text framework that links routine EEG review with the construction of AI-ready datasets. The framework integrates multi-format EEG ingestion, channel standardization, and 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 a standardized set of spectral, temporal, entropy, Hjorth, connectivity, and spike-related descriptors, stored alongside clinical descriptions in a portable JSON schema. This yields aligned feature-text pairs designed to supervise multimodal EEG-language models. The framework also includes an auto-report module that couples an ensemble of convolutional networks with a large language model to draft clinical narratives for neurologist review. Using pilot annotations, we describe how EEG-to-Report streamlines annotation workflows and produces editable draft reports, providing a reusable foundation for automated EEG reporting systems.", "url": "https://wpnews.pro/news/eeg-to-report-an-annotation-and-feature-text-framework-for-training-language-on", "canonical_source": "https://arxiv.org/abs/2608.26153", "published_at": "2026-08-28 04:00:00+00:00", "updated_at": "2026-08-28 04:18:58.853351+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-tools", "ai-research"], "entities": ["EEG-to-Report"], "alternates": {"html": "https://wpnews.pro/news/eeg-to-report-an-annotation-and-feature-text-framework-for-training-language-on", "markdown": "https://wpnews.pro/news/eeg-to-report-an-annotation-and-feature-text-framework-for-training-language-on.md", "text": "https://wpnews.pro/news/eeg-to-report-an-annotation-and-feature-text-framework-for-training-language-on.txt", "jsonld": "https://wpnews.pro/news/eeg-to-report-an-annotation-and-feature-text-framework-for-training-language-on.jsonld"}}