arXiv 2602.047692026-02-09cs.LG

NeuroCanvas：通过重新表述多通道EEG为图像实现由VLLM驱动的鲁棒癫痫检测

NeuroCanvas: VLLM-Powered Robust Seizure Detection by Reformulating Multichannel EEG as Image

MINDxAI Lab, Industrial \& Systems Engineering Department, University of Louisville；UNITES Lab, University of North Carolina at Chapel Hill

Yan Chen, Jie Peng, Moajjem Hossain Chowdhury, Tianlong Chen, Yunmei Liu

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AI总结：

NeuroCanvas通过重新表述多通道EEG为图像，利用VLLM实现高效且鲁棒的癫痫检测，提升F1分数20%并降低88%的推理延迟。

摘要：

从脑电图（EEG）中准确且及时地检测癫痫发作对于临床干预至关重要，但对长期记录进行人工审查是劳动密集型的。最近将EEG信号编码到大语言模型（LLMs）中的努力在处理不同患者神经信号方面显示出潜力，但仍然存在两个重大挑战：（1）多通道异质性，因为与癫痫相关的信息在EEG通道之间差异显著；（2）计算效率低下，因为EEG信号需要被编码成大量标记以进行预测。为了解决这些问题，我们提出了EEG信号并提出了新的NeuroCanvas框架。具体而言，NeuroCanvas由两个模块组成：（i）熵引导的通道选择器（ECS）选择输入到LLM的癫痫相关通道，（ii）神经信号画布（CNS）将所选多通道异质EEG信号转换为结构化的视觉表示。ECS模块缓解了多通道异质性问题，而CNS使用紧凑的视觉标记来表示EEG信号，从而提高了计算效率。我们评估了NeuroCanvas在多个癫痫检测数据集上的表现，证明了F1分数提高了20%，推理延迟减少了88%。这些结果突显了NeuroCanvas作为实时和资源高效的癫痫检测解决方案在临床实践中的可扩展性和有效性。

英文摘要：

Accurate and timely seizure detection from Electroencephalography (EEG) is critical for clinical intervention, yet manual review of long-term recordings is labor-intensive. Recent efforts to encode EEG signals into large language models (LLMs) show promise in handling neural signals across diverse patients, but two significant challenges remain: (1) multi-channel heterogeneity, as seizure-relevant information varies substantially across EEG channels, and (2) computing inefficiency, as the EEG signals need to be encoded into a massive number of tokens for the prediction. To address these issues, we draw the EEG signal and propose the novel NeuroCanvas framework. Specifically, NeuroCanvas consists of two modules: (i) The Entropy-guided Channel Selector (ECS) selects the seizure-relevant channels input to LLM and (ii) the following Canvas of Neuron Signal (CNS) converts selected multi-channel heterogeneous EEG signals into structured visual representations. The ECS module alleviates the multi-channel heterogeneity issue, and the CNS uses compact visual tokens to represent the EEG signals that improve the computing efficiency. We evaluate NeuroCanvas across multiple seizure detection datasets, demonstrating a significant improvement of 20% in F1 score and reductions of 88% in inference latency. These results highlight NeuroCanvas as a scalable and effective solution for real-time and resource-efficient seizure detection in clinical practice.

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