Eeg-Based Video Identification Using Graph Signal Modeling and Graph Convolutional Neural Network

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. Experim...

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Bibliographic Details
Main Authors: Jang, Soobeom, Moon, Seong-Eun, Lee, Jong-Seok
Format: Conference Proceeding
Language:English
Subjects:
Brain modeling
Convolution
Convolutional neural networks
deep learning
Electrodes
Electroencephalography
graph signal
graph theory
Machine learning
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8462207