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

arXiv: https://arxiv.org/abs/2602.04769 · HTML: https://mlsanity.com/paper/2602.04769/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 50 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
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- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Quantifying Attention Flow in Transformers](https://mlsanity.com/paper/2005.00928/) (2020)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [Language Models Still Struggle to Zero-shot Reason about Time Series](https://mlsanity.com/paper/2404.11757/) (2024)
- [VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2408.17253/) (2024)
- [NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals](https://mlsanity.com/paper/2409.00101/) (2024)
- [The Temple University Hospital Seizure Detection Corpus](https://mlsanity.com/paper/1801.08085/) (2018)
- [Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis](https://mlsanity.com/paper/2104.08336/) (2021)
- [Revisiting Token Pruning for Object Detection and Instance Segmentation](https://mlsanity.com/paper/2306.07050/) (2023)
- [Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Acceleration](https://mlsanity.com/paper/2412.13180/) (2024)
- [Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review](https://mlsanity.com/paper/2002.01925/) (2020)
- [A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization](https://mlsanity.com/paper/2411.06018/) (2024)
- [EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation](https://mlsanity.com/paper/2401.18006/) (2024)
- [Harnessing Vision Models for Time Series Analysis: A Survey](https://mlsanity.com/paper/2502.08869/) (2025)
- [Neural Memory Networks for Seizure Type Classification](https://mlsanity.com/paper/1912.04968/) (2019)
- [TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning](https://mlsanity.com/paper/2506.13705/) (2025)
- [On the Surprising Effectiveness of Attention Transfer for Vision Transformers](https://mlsanity.com/paper/2411.09702/) (2024)
- [UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding](https://mlsanity.com/paper/2506.18962/) (2025)
- [Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture](https://mlsanity.com/paper/2105.13854/) (2021)
- [REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates](https://mlsanity.com/paper/2406.16906/) (2024)
- [Harnessing Vision-Language Models for Time Series Anomaly Detection](https://mlsanity.com/paper/2506.06836/) (2025)
- [Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting](https://mlsanity.com/paper/2201.08780/) (2022)
- [EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks](https://mlsanity.com/paper/2509.15857/) (2025)
- [WaveMind: Towards a Conversational EEG Foundation Model Aligned to Textual and Visual Modalities](https://mlsanity.com/paper/2510.00032/) (2025)

## Abstract (arXiv)

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.