DocumentCode :
3492827
Title :
Genetic feature selection in EEG-based motion sickness estimation
Author :
Wei, Chun-Shu ; Ko, Li-Wei ; Chuang, Shang-Wen ; Jung, Tzyy-Ping ; Lin, Chin-Teng
Author_Institution :
Dept. of Electr. Eng., Nat. Chiao-Tung Univ., Hsinchu, Taiwan
fYear :
2011
fDate :
July 31 2011-Aug. 5 2011
Firstpage :
365
Lastpage :
369
Abstract :
Motion sickness is a common symptom that occurs when the brain receives conflicting information about the sensation of movement. Many motion sickness biomarkers have been identified, and electroencephalogram (EEG)-based motion sickness level estimation was found feasible in our previous study. This study employs genetic feature selection to find a subset of EEG features that can further improve estimation performance over the correlation-based method reported in the previous studies. The features selected by genetic feature selection were very different from those obtained by correlation analysis. Results of this study demonstrate that genetic feature selection is a very effective method to optimize the estimation of motion-sickness level. This demonstration could lead to a practical system for noninvasive monitoring of the motion sickness of individuals in real-world environments.
Keywords :
electroencephalography; feature extraction; medical signal processing; motion estimation; EEG-based motion sickness level estimation; correlation analysis; electroencephalogram; estimation performance improvement; genetic feature selection; Correlation; Educational institutions; Electroencephalography; Estimation; Feature extraction; Genetic algorithms; Genetics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
ISSN :
2161-4393
Print_ISBN :
978-1-4244-9635-8
Type :
conf
DOI :
10.1109/IJCNN.2011.6033244
Filename :
6033244
Link To Document :
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