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 proceedingConference article published in proceeding or bookAcademic researchpeer-review

51 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 languageEnglish
Title of host publicationMultiMedia Modeling - 24th International Conference, MMM 2018, Proceedings
EditorsAhmed Elgammal, Thanarat H. Chalidabhongse, Supavadee Aramvith, Yo-Sung Ho, Klaus Schoeffmann, Chong Wah Ngo, Noel E. O'Connor, Moncef Gabbouj
PublisherSpringer-Verlag
Pages82-93
Number of pages12
ISBN (Print)9783319735993
DOIs
Publication statusPublished - 1 Jan 2018
Event24th International Conference on MultiMedia Modeling, MMM 2018 - Bangkok, Thailand
Duration: 5 Feb 20187 Feb 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10705 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on MultiMedia Modeling, MMM 2018
Country/TerritoryThailand
CityBangkok
Period5/02/187/02/18

Keywords

  • Data augmentation
  • EEG
  • Emotion recognition

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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