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

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.

Original languageEnglish
Pages (from-to)264-275
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Volume26
Issue number1
DOIs
Publication statusPublished - 2022 Jan 1

Keywords

  • 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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