CerebraGloss: Instruction-Tuning a Large Vision-Language Model for Fine-Grained Clinical EEG Interpretation

Wei Gu, Luo Tianming, Qiran Zhang, Mohan Ye, Xiao Shen, Wenxin Chen, Yunhuan Li, Yichen Zhang, Jing Hong, Bao-liang Lu, Wei-Long Zheng

Keywords: large vision-language model, instruction-tuning, EEG, clinical

TL;DR: We present CerebraGloss, the first instruction-tuned LVLM for fine-grained clinical EEG analysis, enabled by a novel automated data generation pipeline and evaluated on our new comprehensive benchmark, CerebraGloss-Bench.

Abstract: Interpreting clinical electroencephalography (EEG) is a laborious, subjective process, and existing computational models are limited to narrow classification tasks rather than holistic interpretation. A key bottleneck for applying powerful Large Vision-Language Models (LVLMs) to this domain is the scarcity of datasets pairing EEG visualizations with fine-grained, expert-level annotations. We address this by introducing CerebraGloss, an instruction-tuned LVLM for nuanced EEG interpretation. We first introduce a novel, automated data generation pipeline, featuring a bespoke YOLO-based waveform detector, to programmatically create a large-scale corpus of EEG-text instruction data. Using this data, we develop CerebraGloss, the first model of its kind capable of unified, generative analysis—performing tasks from detailed waveform description to multi-turn, context-aware dialogue. To evaluate this new capability, we construct and release CerebraGloss-Bench, a comprehensive benchmark for open-ended EEG interpretation. CerebraGloss demonstrates strong performance, surpassing leading LVLMs, including proprietary models like GPT-5, on this benchmark and achieving a new state-of-the-art on the TUSZ seizure detection task. Models, benchmark and tools are available at https://github.com/iewug/CerebraGloss.

Primary Area: foundation or frontier models, including LLMs

Submission Number: 8528