The EEG Signal Preprocessing Based on Empirical Mode Decomposition
The electroencephalogram (EEG) is widely used by physicians for interpretation and identification of physiological and pathological phenomena. However, the EEG signals are often corrupted by power line interferences noise and EMG induced noise. These artifacts strongly influence the utility of recorded EEGs and need to be removed for better clinical diagnosis. How to eliminate the effect of the noise is an important preprocessing problem in signal processing. In this paper, a novel and efficient power interferences reduction algorithm by the recently developed empirical mode decomposition (EMD) for the EEG signal is proposed. The principle of this method consists of decompositions of the EEG signal into a limited number of intrinsic mode function (IMF). This algorithm can effectively detect, separate and remove a wide variety of artifacts from EEG recording. Experimental results show that the proposed EMDbased algorithm is possible to achieve an excellent balance between suppresses power interference and EMG noise effectively and preserves as many target characteristics of original signal as possible.
EEG empirical mode decomposition power interference instantaneous frequency denoising
Zhang De-xiang Wu Xiao-pei Guo Xiao-jing
The Institute of Electronic Science and Technology Anhui University Hefei, 230039, China Key Lab. of Intelligent Computing and Signal Processing Anhui University Hefei 230039, China
国际会议
上海
英文
2131-2134
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）