Abstract—Identifying mammalian vigilance states has recently become an important topic in biological science research. The biological researchers concern not only to improve the accuracy rate for classifying the vigilance states, but also to extract the meaningful frequency bands. In this study, we propose a novel feature selection to extract the critical frequency bands of rat’s EEG signals. The proposed algorithm adopts the concept of neighborhood relation during adding and eliminating a candidate feature. In the experiments, the proposed method shows better accuracy rate, and find out the feature subset which locate on the critical frequency bands for recognizing rat’s vigilance states.

Index Terms—Feature selection, frequency band, pattern recognition, vigilance states.

Chien-Hsing Chou, Hsien-Pang Tai, and Ke-Wei Chen are with the Department of Electrical Engineering, Tamkang University, Taiwan (e-mail: chchou@mail.tku.edu.tw).
Chung-Chih Kuo is with the Institute of Neuroscience, Tzu Chi University, Taiwan.
Zong-En Yu is with the Department of Electrical Engineering, National Taiwan University, Taiwan.

Cite:Chien-Hsing Chou, Chung-Chih Kuo, Zong-En Yu, Hsien-Pang Tai, and Ke-Wei Chen, "Extracting the Critical Frequency Bands to Classify Vigilance States of Rats by Using a Novel Feature Selection Algorithm," International Journal of Computer and Communication Engineering vol. 2, no. 3, pp. 277-280, 2013.