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Kim SH, Yang HJ, Nguyen NAT, et al. WeDea: A New EEG-Based Framework for Emotion Recognition. IEEE J Biomed Health Inform. 2022;26(1):264-275doi: 10.1109/JBHI.2021.3091187.
Kim, S. H., Yang, H. J., Nguyen, N. A. T., Prabhakar, S. K., & Lee, S. W. (2022). WeDea: A New EEG-Based Framework for Emotion Recognition. IEEE journal of biomedical and health informatics, 26(1), 264-275. https://doi.org/10.1109/JBHI.2021.3091187
Kim, Sun-Hee, et al. "WeDea: A New EEG-Based Framework for Emotion Recognition." IEEE journal of biomedical and health informatics vol. 26,1 (2022): 264-275. doi: https://doi.org/10.1109/JBHI.2021.3091187
Kim SH, Yang HJ, Nguyen NAT, Prabhakar SK, Lee SW. WeDea: A New EEG-Based Framework for Emotion Recognition. IEEE J Biomed Health Inform. 2022 Jan;26(1):264-275. doi: 10.1109/JBHI.2021.3091187. Epub 2022 Jan 17. PMID: 34156955.
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IEEE J Biomed Health Inform. 2022 Jan;26(1):264-275. doi: 10.1109/JBHI.2021.3091187. Epub 2022 Jan 17.

WeDea: A New EEG-Based Framework for Emotion Recognition.

IEEE journal of biomedical and health informatics

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

PMID: 34156955 DOI: 10.1109/JBHI.2021.3091187

Abstract

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.

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