@inproceedings{51c7567f7f9845ab98b6693db0d94f7c,
title = "Normalized mutual information feature selection for electroencephalogram data based on grassberger entropy estimator",
abstract = "Recently, Electroencephalogram (EEG) has become increasingly important in the role of psychiatric diagnosis and emotion recognition. However, many irrelevant features make it difficult to identify patterns accurately. Obtaining valid features from electroencephalogram can improve the classification and generalization performance. In this paper, an improved normalized mutual information feature selection algorithm which is based on Grassberger entropy estimator (G-NMIFS) is proposed for EEG data. We employ the k-Nearest Neighbor (kNN), Support Vector Machine (SVM), and Na{\"i}ve Bayes methods to compare the proposed approach with normalized mutual information feature selection using Na{\"i}ve estimator and Miller-adjust method. Experimental results on two EEG data sets show that the proposed method can select relevant subsets and improve classification performance effectively.",
keywords = "EEG, Grassberger entropy, feature selection, mutual information",
author = "Xiaowei Zhang and Yuan Yao and Manman Wang and Jian Shen and Lei Feng 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.2017.8217730",
language = "English",
series = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "648--652",
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",
}