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

Soobeom Jang; Seong Eun Moon; Jong Seok Lee

doi:10.1109/ICASSP.2018.8462207

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

Soobeom Jang, Seong Eun Moon, Jong Seok Lee

School of Integrated Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

46 Citations (Scopus)

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.

Original language	English
Title of host publication	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3066-3070
Number of pages	5
ISBN (Print)	9781538646588
DOIs	https://doi.org/10.1109/ICASSP.2018.8462207
Publication status	Published - 2018 Sept 10
Event	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: 2018 Apr 15 → 2018 Apr 20

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2018-April
ISSN (Print)	1520-6149

Conference

Conference	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/Territory	Canada
City	Calgary
Period	18/4/15 → 18/4/20

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2018.8462207

Cite this

Jang, S., Moon, S. E., & Lee, J. S. (2018). Eeg-Based Video Identification Using Graph Signal Modeling and Graph Convolutional Neural Network. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 3066-3070). Article 8462207 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8462207

Jang, Soobeom ; Moon, Seong Eun ; Lee, Jong Seok. / Eeg-Based Video Identification Using Graph Signal Modeling and Graph Convolutional Neural Network. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 3066-3070 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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title = "Eeg-Based Video Identification Using Graph Signal Modeling and Graph Convolutional Neural Network",

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.",

keywords = "Deep learning, Electroencephalography, Graph signal, Graph theory",

author = "Soobeom Jang and Moon, {Seong Eun} and Lee, {Jong Seok}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",

year = "2018",

month = sep,

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doi = "10.1109/ICASSP.2018.8462207",

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series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

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Jang, S, Moon, SE & Lee, JS 2018, Eeg-Based Video Identification Using Graph Signal Modeling and Graph Convolutional Neural Network. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings., 8462207, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2018-April, Institute of Electrical and Electronics Engineers Inc., pp. 3066-3070, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 18/4/15. https://doi.org/10.1109/ICASSP.2018.8462207

Eeg-Based Video Identification Using Graph Signal Modeling and Graph Convolutional Neural Network. / Jang, Soobeom; Moon, Seong Eun; Lee, Jong Seok.
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3066-3070 8462207 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Eeg-Based Video Identification Using Graph Signal Modeling and Graph Convolutional Neural Network

AU - Jang, Soobeom

AU - Moon, Seong Eun

AU - Lee, Jong Seok

PY - 2018/9/10

Y1 - 2018/9/10

N2 - 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.

AB - 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.

KW - Deep learning

KW - Electroencephalography

KW - Graph signal

KW - Graph theory

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DO - 10.1109/ICASSP.2018.8462207

M3 - Conference contribution

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SN - 9781538646588

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 3066

EP - 3070

BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018

Y2 - 15 April 2018 through 20 April 2018

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Jang S, Moon SE, Lee JS. Eeg-Based Video Identification Using Graph Signal Modeling and Graph Convolutional Neural Network. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 3066-3070. 8462207. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2018.8462207

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

Abstract

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All Science Journal Classification (ASJC) codes

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