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

• URL: http://arxiv.org/abs/2602.04769v2
• Date: Mon, 09 Feb 2026 03:38:37 GMT
• Title: NeuroCanvas: VLLM-Powered Robust Seizure Detection by Reformulating Multichannel EEG as Image
• Authors: Yan Chen, Jie Peng, Moajjem Hossain Chowdhury, Tianlong Chen, Yunmei Liu,
• Abstract summary: We present a novel framework for accurate and timely seizure detection from Electroencephalography (EEG)<n>NeuroCanvas consists of two modules: (i) The Entropy-guided Channel Selector (ECS) selects the seizure-relevant channels input to large language models (LLMs) and (ii) the following Canvas of Neuron Signal ( CNS) converts selected multi-channel heterogeneous EEG signals into structured visual representations.<n>We evaluate NeuroCanvas across multiple seizure detection datasets, demonstrating a significant improvement of 20% in F1 score and reductions of 88% in latency.
• Score: 35.12570738980016
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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