@inproceedings{73d1cff48f1a499da90ff2ab91bd5cc5,
title = "Estimation of valence of emotion using two frontal EEG channels",
abstract = "Emotion recognition using EEG signals has become a hot research topic in the last few years. This paper aims at providing a novel method for emotion recognition using less channels of frontal EEG signals. By employing the asymmetry theory of frontal brain, a new method fusing spatial and frequency features was presented, which only adopted two channels of frontal EEG signals at Fp1 and Fp2. In order to estimate the efficiency of the method, a GBDT classifier was evaluated and selected, and the method was implemented on the DEAP database. The maximum and mean classification accuracy were achieved as 76.34% and 75.18% respectively, which exhibited the best result comparing with other related studies. This method is extremely suitable for wearable EEG monitoring applications in human daily life.",
keywords = "DEAP, Emotion recognition, Frontal EEG, GBDT classifier",
author = "Shiyi Wu and Xiangmin Xu and Lin Shu and Bin Hu",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 ; Conference date: 13-11-2017 Through 16-11-2017",
year = "2017",
month = dec,
day = "15",
doi = "10.1109/BIBM.2017.8217815",
language = "English",
series = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1127--1130",
editor = "Illhoi Yoo and Zheng, {Jane Huiru} and Yang Gong and Hu, {Xiaohua Tony} and Chi-Ren Shyu and Yana Bromberg and Jean Gao and Dmitry Korkin",
booktitle = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",
address = "United States",
}