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2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2017)
Kansas City, MO, USA
Nov. 13, 2017 to Nov. 16, 2017
ISBN: 978-1-5090-3051-4
pp: 648-652
Xiaowei Zhang , Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University
Yuan Yao , Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University
Manman Wang , Shenzhen city Tencent computer system Co. Ltd
Jian Shen , Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University
Lei Feng , The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Anding Hospital, Capital Medical University, Beijing, China
Bin Hu , Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University
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 Naïve Bayes methods to compare the proposed approach with normalized mutual information feature selection using Naïve 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.
INDEX TERMS
Mutual information, Feature extraction, Electroencephalography, Entropy, Estimation, Support vector machines, Electrodes
CITATION

X. Zhang, Y. Yao, M. Wang, J. Shen, L. Feng and B. Hu, "Normalized mutual information feature selection for electroencephalogram data based on grassberger entropy estimator," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 648-652.
doi:10.1109/BIBM.2017.8217730
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