EEG-based Emotion Word Recognition

DOI

10.2991/acaai-18.2018.28How to use a DOI?

Keywords

affective computing; Electroencephalogram(EEG); Emotion words recognition; Feature extraction; Classification; Event Related Potential(ERP)

Abstract

Emotion recognition rapidly gains interest from research community, which gives computer the ability to recognize, understand, express, and adapt to human emotions to help computer more intelligent. Traditional emotion recognition modalities (e.g. face and voice) always could not identify the totally true emotion state. Recently, with the development of electroencephalogram (EEG) equipment, utilizing EEG signals to build emotion recognition system is more and more feasible and meaningful. In this paper, an abstract emotional words classification task is designed, which requires participants judge the emotion polarity (positive or negative) for given words. Based on EEG signals, a series of study is re-explored, including data collecting, feature extraction, feature selection, and classification model. Furthermore, the Event Related Potential (ERP) components and behavioral data analysis uncover some meaningful conclusion on emotion recognition.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Weiwei Dong
AU  - Panpan Wang
AU  - Yazhou Zhang
AU  - Tianshu Wang
AU  - Jiabin Niu
AU  - Shengnan Zhang
PY  - 2018/03
DA  - 2018/03
TI  - EEG-based Emotion Word Recognition
BT  - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)
PB  - Atlantis Press
SP  - 121
EP  - 124
SN  - 1951-6851
UR  - https://doi.org/10.2991/acaai-18.2018.28
DO  - 10.2991/acaai-18.2018.28
ID  - Dong2018/03
ER  -