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

2026-02-04

AI Summary

This work proposes a novel and efficient framework for multichannel EEG-based seizure detection, addressing the challenges of high inter-channel heterogeneity and low computational efficiency. The approach first employs an entropy-guided channel selector to identify the most informative EEG channels, then reformulates the selected multichannel signals into structured images. For the first time, these images are fed into a vision-language large model (VLLM), enhanced with compact visual token representations to reduce redundancy. This strategy achieves both high detection accuracy and substantial gains in inference speed. Evaluated across multiple epilepsy datasets, the method improves F1 scores by 20% while reducing inference latency by 88%, effectively balancing diagnostic precision with practical deployment efficiency.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• seizure detection
• multichannel EEG
• channel heterogeneity
• computing inefficiency
• EEG encoding

Innovation

• NeuroCanvas
• EEG-to-image reformulation
• Entropy-guided Channel Selection
• Visual token representation
• Efficient seizure detection