Analysis of Motor Imagery EEG Based on Hilbert-Huang Transform

Xiao Lin Zhu; Jian Ping Liu; Xiao Nan Zhang

doi:10.4028/www.scientific.net/AMR.998-999.833

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 998-999Analysis of Motor Imagery EEG Based on...

Analysis of Motor Imagery EEG Based on Hilbert-Huang Transform

Abstract:

Based on Hilbert-Huang Transform (HHT) theory, we present a method to analyze the electroencephalogram (EEG) signal of right and left hand motor imagery. Firstly, EMD method decomposed EEG signal into a group of intrinsic mode functions (IMFs). The first three IMFs were extracted to denoise. We adopt endpoint Mirror Extension method to relieve the influence on subsequent processing brought by endpoint effect. According to the Hilbert transform, we can obtain the time-frequency distribution. The energy of the first three components is selected as the input of SVM. The results show that EMD is an efficient method to analyze the EEG signal. The proposed method obtains an ideal recognition rate.

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Periodical:

Advanced Materials Research (Volumes 998-999)

Pages:

833-837

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.998-999.833

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Online since:

July 2014

Authors:

Xiao Lin Zhu*, Jian Ping Liu, Xiao Nan Zhang

Keywords:

Empirical Mode Decomposition (EMD), Hilbert-Huang Transform (HHT), Support Vector Machine (SVM)

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