Data augmentation for eeg-based emotion recognition with deep convolutional neural networks

Fang Wang; Sheng Hua Zhong; Jianfeng Peng; Jianmin Jiang; Yan Liu

doi:10.1007/978-3-319-73600-6_8

Data augmentation for eeg-based emotion recognition with deep convolutional neural networks

Fang Wang, Sheng Hua Zhong, Jianfeng Peng, Jianmin Jiang, Yan Liu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

188 Citations (Scopus)

Abstract

Emotion recognition is the task of recognizing a person’s emotional state. EEG, as a physiological signal, can provide more detailed and complex information for emotion recognition task. Meanwhile, EEG can’t be changed and hidden intentionally makes EEG-based emotion recognition achieve more effective and reliable result. Unfortunately, due to the cost of data collection, most EEG datasets have small number of EEG data. The lack of data makes it difficult to predict the emotion states with the deep models, which requires enough number of training data. In this paper, we propose to use a simple data augmentation method to address the issue of data shortage in EEG-based emotion recognition. In experiments, we explore the performance of emotion recognition with the shallow and deep computational models before and after data augmentation on two standard EEG-based emotion datasets. Our experimental results show that the simple data augmentation method can improve the performance of emotion recognition based on deep models effectively.

Original language	English
Title of host publication	MultiMedia Modeling - 24th International Conference, MMM 2018, Proceedings
Editors	Ahmed Elgammal, Thanarat H. Chalidabhongse, Supavadee Aramvith, Yo-Sung Ho, Klaus Schoeffmann, Chong Wah Ngo, Noel E. O'Connor, Moncef Gabbouj
Publisher	Springer-Verlag
Pages	82-93
Number of pages	12
ISBN (Print)	9783319735993
DOIs	https://doi.org/10.1007/978-3-319-73600-6_8
Publication status	Published - 1 Jan 2018
Event	24th International Conference on MultiMedia Modeling, MMM 2018 - Bangkok, Thailand Duration: 5 Feb 2018 → 7 Feb 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10705 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on MultiMedia Modeling, MMM 2018
Country/Territory	Thailand
City	Bangkok
Period	5/02/18 → 7/02/18

Keywords

Data augmentation
EEG
Emotion recognition

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

More information

10.1007/978-3-319-73600-6_8

Cite this

Wang, F., Zhong, S. H., Peng, J., Jiang, J., & Liu, Y. (2018). Data augmentation for eeg-based emotion recognition with deep convolutional neural networks. In A. Elgammal, T. H. Chalidabhongse, S. Aramvith, Y.-S. Ho, K. Schoeffmann, C. W. Ngo, N. E. O'Connor, & M. Gabbouj (Eds.), MultiMedia Modeling - 24th International Conference, MMM 2018, Proceedings (pp. 82-93). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10705 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-73600-6_8

Wang, Fang ; Zhong, Sheng Hua ; Peng, Jianfeng et al. / Data augmentation for eeg-based emotion recognition with deep convolutional neural networks. MultiMedia Modeling - 24th International Conference, MMM 2018, Proceedings. editor / Ahmed Elgammal ; Thanarat H. Chalidabhongse ; Supavadee Aramvith ; Yo-Sung Ho ; Klaus Schoeffmann ; Chong Wah Ngo ; Noel E. O'Connor ; Moncef Gabbouj. Springer-Verlag, 2018. pp. 82-93 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{2bf6ab32b61a458ba46cc41942142b9c,

title = "Data augmentation for eeg-based emotion recognition with deep convolutional neural networks",

abstract = "Emotion recognition is the task of recognizing a person{\textquoteright}s emotional state. EEG, as a physiological signal, can provide more detailed and complex information for emotion recognition task. Meanwhile, EEG can{\textquoteright}t be changed and hidden intentionally makes EEG-based emotion recognition achieve more effective and reliable result. Unfortunately, due to the cost of data collection, most EEG datasets have small number of EEG data. The lack of data makes it difficult to predict the emotion states with the deep models, which requires enough number of training data. In this paper, we propose to use a simple data augmentation method to address the issue of data shortage in EEG-based emotion recognition. In experiments, we explore the performance of emotion recognition with the shallow and deep computational models before and after data augmentation on two standard EEG-based emotion datasets. Our experimental results show that the simple data augmentation method can improve the performance of emotion recognition based on deep models effectively.",

keywords = "Data augmentation, EEG, Emotion recognition",

author = "Fang Wang and Zhong, {Sheng Hua} and Jianfeng Peng and Jianmin Jiang and Yan Liu",

year = "2018",

month = jan,

day = "1",

doi = "10.1007/978-3-319-73600-6_8",

language = "English",

isbn = "9783319735993",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer-Verlag",

pages = "82--93",

editor = "Ahmed Elgammal and Chalidabhongse, {Thanarat H.} and Supavadee Aramvith and Yo-Sung Ho and Klaus Schoeffmann and Ngo, {Chong Wah} and O'Connor, {Noel E.} and Moncef Gabbouj",

booktitle = "MultiMedia Modeling - 24th International Conference, MMM 2018, Proceedings",

note = "24th International Conference on MultiMedia Modeling, MMM 2018 ; Conference date: 05-02-2018 Through 07-02-2018",

}

Wang, F, Zhong, SH, Peng, J, Jiang, J & Liu, Y 2018, Data augmentation for eeg-based emotion recognition with deep convolutional neural networks. in A Elgammal, TH Chalidabhongse, S Aramvith, Y-S Ho, K Schoeffmann, CW Ngo, NE O'Connor & M Gabbouj (eds), MultiMedia Modeling - 24th International Conference, MMM 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10705 LNCS, Springer-Verlag, pp. 82-93, 24th International Conference on MultiMedia Modeling, MMM 2018, Bangkok, Thailand, 5/02/18. https://doi.org/10.1007/978-3-319-73600-6_8

Data augmentation for eeg-based emotion recognition with deep convolutional neural networks. / Wang, Fang; Zhong, Sheng Hua; Peng, Jianfeng et al.
MultiMedia Modeling - 24th International Conference, MMM 2018, Proceedings. ed. / Ahmed Elgammal; Thanarat H. Chalidabhongse; Supavadee Aramvith; Yo-Sung Ho; Klaus Schoeffmann; Chong Wah Ngo; Noel E. O'Connor; Moncef Gabbouj. Springer-Verlag, 2018. p. 82-93 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10705 LNCS).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Data augmentation for eeg-based emotion recognition with deep convolutional neural networks

AU - Wang, Fang

AU - Zhong, Sheng Hua

AU - Peng, Jianfeng

AU - Jiang, Jianmin

AU - Liu, Yan

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Emotion recognition is the task of recognizing a person’s emotional state. EEG, as a physiological signal, can provide more detailed and complex information for emotion recognition task. Meanwhile, EEG can’t be changed and hidden intentionally makes EEG-based emotion recognition achieve more effective and reliable result. Unfortunately, due to the cost of data collection, most EEG datasets have small number of EEG data. The lack of data makes it difficult to predict the emotion states with the deep models, which requires enough number of training data. In this paper, we propose to use a simple data augmentation method to address the issue of data shortage in EEG-based emotion recognition. In experiments, we explore the performance of emotion recognition with the shallow and deep computational models before and after data augmentation on two standard EEG-based emotion datasets. Our experimental results show that the simple data augmentation method can improve the performance of emotion recognition based on deep models effectively.

AB - Emotion recognition is the task of recognizing a person’s emotional state. EEG, as a physiological signal, can provide more detailed and complex information for emotion recognition task. Meanwhile, EEG can’t be changed and hidden intentionally makes EEG-based emotion recognition achieve more effective and reliable result. Unfortunately, due to the cost of data collection, most EEG datasets have small number of EEG data. The lack of data makes it difficult to predict the emotion states with the deep models, which requires enough number of training data. In this paper, we propose to use a simple data augmentation method to address the issue of data shortage in EEG-based emotion recognition. In experiments, we explore the performance of emotion recognition with the shallow and deep computational models before and after data augmentation on two standard EEG-based emotion datasets. Our experimental results show that the simple data augmentation method can improve the performance of emotion recognition based on deep models effectively.

KW - Data augmentation

KW - EEG

KW - Emotion recognition

UR - http://www.scopus.com/inward/record.url?scp=85042089206&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-73600-6_8

DO - 10.1007/978-3-319-73600-6_8

M3 - Conference article published in proceeding or book

AN - SCOPUS:85042089206

SN - 9783319735993

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 82

EP - 93

BT - MultiMedia Modeling - 24th International Conference, MMM 2018, Proceedings

A2 - Elgammal, Ahmed

A2 - Chalidabhongse, Thanarat H.

A2 - Aramvith, Supavadee

A2 - Ho, Yo-Sung

A2 - Schoeffmann, Klaus

A2 - Ngo, Chong Wah

A2 - O'Connor, Noel E.

A2 - Gabbouj, Moncef

PB - Springer-Verlag

T2 - 24th International Conference on MultiMedia Modeling, MMM 2018

Y2 - 5 February 2018 through 7 February 2018

ER -

Wang F, Zhong SH, Peng J, Jiang J, Liu Y. Data augmentation for eeg-based emotion recognition with deep convolutional neural networks. In Elgammal A, Chalidabhongse TH, Aramvith S, Ho YS, Schoeffmann K, Ngo CW, O'Connor NE, Gabbouj M, editors, MultiMedia Modeling - 24th International Conference, MMM 2018, Proceedings. Springer-Verlag. 2018. p. 82-93. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-73600-6_8

Data augmentation for eeg-based emotion recognition with deep convolutional neural networks

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

More information

Other files and links

Fingerprint

Cite this