@inproceedings{7010cb602adb4d92a7e4130fad3c3a0e,
title = "Genetic feature selection in EEG-based motion sickness estimation",
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.",
author = "Chun-Shu Wei and Li-Wei Ko and Chuang, {Shang Wen} and Jung, {Tzyy Ping} and Chin-Teng Lin",
year = "2011",
month = aug,
day = "2",
doi = "10.1109/IJCNN.2011.6033244",
language = "English",
isbn = "9781457710865",
series = "Proceedings of the International Joint Conference on Neural Networks",
pages = "365--369",
booktitle = "2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program",
note = "2011 International Joint Conference on Neural Network, IJCNN 2011 ; Conference date: 31-07-2011 Through 05-08-2011",
}