Wan Baikun, Zhou Zhongxing, Xu Lifeng, Ming Dong, Qi Hongzhi, Cheng Longlong
Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, PR China.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2232-5. doi: 10.1109/IEMBS.2009.5335012.
The aim of this paper is to investigate the possibility of using empirical mode decomposition (EMD) method in detecting the desynchronized mu rhythm of motor imagery EEG signal. A number of EEG studies have identified the mu rhythm desynchronization a reliable EEG pattern for brain-computer interface. Considering the non-stationary characteristics of the motor imagery EEG, the EMD method is proposed to decompose the EEG signal into intrinsic mode functions (IMFs). By analyzing the power spectral density (PSD) of the IMFs, the characteristics one representing mu rhythm oscillations can be detected. Then by Hilbert transformation, the event-related desynchronization phenomenon can be found by the envelope of the characteristics IMF. Results demonstrate that the EMD method is an effective time-frequency analysis tool for non-stationary EEG signal.
本文旨在研究使用经验模态分解（EMD）方法检测运动想象脑电信号中去同步化μ节律的可能性。大量脑电研究已将μ节律去同步化确定为脑机接口的一种可靠脑电模式。考虑到运动想象脑电的非平稳特性，提出了EMD方法将脑电信号分解为固有模态函数（IMF）。通过分析IMF的功率谱密度（PSD），可以检测到代表μ节律振荡的特征成分。然后通过希尔伯特变换，利用特征IMF的包络发现事件相关去同步化现象。结果表明，EMD方法是一种用于非平稳脑电信号的有效时频分析工具。
