WeDea: A New EEG-Based Framework for Emotion Recognition

Sun Hee Kim, Hyung Jeong Yang, Ngoc Anh Thi Nguyen, Sunil Kumar Prabhakar, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


With the development of sensing technologies and machine learning, techniques that can identify emotions and inner states of a human through physiological signals, known as electroencephalography (EEG), have been actively developed and applied to various domains, such as automobiles, robotics, healthcare, and customer-support services. Thus, the demand for acquiring and analyzing EEG signals in real-time is increasing. In this paper, we aimed to acquire a new EEG dataset based on the discrete emotion theory, termed as WeDea (Wireless-based eeg Data for emotion analysis), and propose a new combination for WeDea analysis. For the collected WeDea dataset, we used video clips as emotional stimulants that were selected by 15 volunteers. Consequently, WeDea is a multi-way dataset measured while 30 subjects are watching the selected 79 video clips under five different emotional states using a convenient portable headset device. Furthermore, we designed a framework for recognizing human emotional state using this new database. The practical results for different types of emotions have proven that WeDea is a promising resource for emotion analysis and can be applied to the field of neuroscience.

Original languageEnglish
Pages (from-to)264-275
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
Publication statusPublished - 2022 Jan 1

Bibliographical note

Funding Information:
Manuscript received March 8, 2021; revised May 27, 2021; accepted June 14, 2021. Date of publication June 22, 2021; date of current version January 5, 2022. This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) under Grant NRF-2018R1A2B6006046, in part by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant 102.01-2020.27, in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) under Grant NRF-2020R1A4A1019191, and in part by the IITP funded by the Korea Government through the Department of Artificial Intelligence, Korea University under Grant 2019-0-00079. (Corresponding author: Seong-Whan Lee).

Publisher Copyright:
© 2013 IEEE.


  • Electroencephalography
  • artifact removal
  • deep learning
  • emotion recognition
  • feature extraction
  • wireless devices

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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