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	Classifying Motor Imagery EEG by Empirical Mode Decomposition Based on Spatial-Time-Frequency Joint Analysis Approach
	Pengfei Wei; Qiuhua Li; Guanglin Li
	2009
会议名称	2009 International Conference on Future BioMedical Information Engineering, FBIE 2009
英文摘要	A novel spatial-time-frequency approach to classify the different mental task in brain computer interface was presented. A high resolution time-frequency spectral was achieved by using Empirical Mode Decomposition and Hilbert-Huang Transform, and the subject specific spatial-timefrequency joint features were extracted from the restricted spectral of multi-channel EEG recordings. A weighting synthetic classifier was built and used to identify the classes of the imaged motions The test results in four subjects showed that the classification accuracy varied between 77.0% and 95.0%, with an average of 85.9%, which suggested that the present method can achieve a reasonable performance in identifying imaged motions compared with previous methods.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/2590]
专题	深圳先进技术研究院_医工所
作者单位	2009
推荐引用方式 GB/T 7714	Pengfei Wei,Qiuhua Li,Guanglin Li. Classifying Motor Imagery EEG by Empirical Mode Decomposition Based on Spatial-Time-Frequency Joint Analysis Approach[C]. 见:2009 International Conference on Future BioMedical Information Engineering, FBIE 2009.

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