TY - JOUR
T1 - WeDea
T2 - A New EEG-Based Framework for Emotion Recognition
AU - Kim, Sun Hee
AU - Yang, Hyung Jeong
AU - Nguyen, Ngoc Anh Thi
AU - Prabhakar, Sunil Kumar
AU - Lee, Seong Whan
N1 - 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.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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.
AB - 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.
KW - Electroencephalography
KW - artifact removal
KW - deep learning
KW - emotion recognition
KW - feature extraction
KW - wireless devices
UR - http://www.scopus.com/inward/record.url?scp=85112424934&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2021.3091187
DO - 10.1109/JBHI.2021.3091187
M3 - Article
C2 - 34156955
AN - SCOPUS:85112424934
VL - 26
SP - 264
EP - 275
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
IS - 1
ER -
