# EEG-based video identification using graph signal modeling and graph convolutional neural network

arXiv: https://arxiv.org/abs/1809.04229 · HTML: https://mlsanity.com/paper/1809.04229/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 9 of 9)

- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface](https://mlsanity.com/paper/2105.02786/) (2021)
- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification](https://mlsanity.com/paper/2202.02472/) (2022)
- [Graph Neural Network-based EEG Classification: A Survey](https://mlsanity.com/paper/2310.02152/) (2023)
- [Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks](https://mlsanity.com/paper/2101.07069/) (2021)
- [Dynamic Graph Modeling of Simultaneous EEG and Eye-tracking Data for Reading Task Identification](https://mlsanity.com/paper/2102.11922/) (2021)
- [Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection](https://mlsanity.com/paper/2501.15062/) (2025)
- [Emotional EEG Classification using Upscaled Connectivity Matrices](https://mlsanity.com/paper/2502.07843/) (2025)

## Abstract (arXiv)

This paper proposes a novel graph signal-based deep learning method for electroencephalography (EEG) and its application to EEG-based video identification. We present new methods to effectively represent EEG data as signals on graphs, and learn them using graph convolutional neural networks. Experimental results for video identification using EEG responses obtained while watching videos show the effectiveness of the proposed approach in comparison to existing methods. Effective schemes for graph signal representation of EEG are also discussed.