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《Beijing Biomedical Engineering》 2007-04
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High-dimensional EEG features selection based on random forests algorithm

LI Fei,GAO Xiaorong,GAO Shangkai Department of Biomedical Engineering,Tsinghua University,Beijing 100084  
EEG features classification is one of the most important tasks in the research on brain-computer interface.There are more and more feature extraction methods to extract lots of information from the mult-channel EEG signal.In this paper,Random forests algorithm is used to analyze the high-dimensional EEG features from the motor imagery experiment.An effective features selection method based on the times that features were used in the growing process of random trees is proposed.The method enhances the accuracy and speed of classification.Also it is used to select EEG leads in EEG classification.
【Fund】: 北京市自然科学基金(30051001);; 863项目(2006AA01Z134)资助
【CateGory Index】: R318
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