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

2018/09/12 by Soobeom Jang, Jang, Soobeom, Seong-Eun Moon +3
Computer Science · Neuroscience · #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Neural Networks and Reservoir Computing #Neural dynamics and brain function #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1809.04229

openalex publication_date 2018/09/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
• Semi-Supervised Classification with Graph Convolutional Networks
• Deep learning with convolutional neural networks for EEG decoding and visualization

Related

• EEG-based Emotional Video Classification via Learning Connectivity Structure
• Graph Neural Network-based EEG Classification: A Survey
• EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals
• Graph Convolutional Neural Networks for analysis of EEG signals, BCI application
• Analyzing EEG Data with Machine and Deep Learning: A Benchmark