A Time-Frequency approach for EEG signal segmentation

Journal of Artificial Intelligence and Data Mining

Milad Azarbad¹  A Ebrahimzadeh²  Saeid Sanei³  Hamed Azami⁴ 

[1] Babol University of Technology;Department of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran;Faculty of Engineering and Physical Sciences, University of Surrey;Iran University of Science and Technology;

关键词: EEG signal segmentation;    time-frequency;    empirical mode decomposition (EMD);    singular spectrum analysis (SSA);    Hilbert-Huang transform (HHT);   

DOI  :  10.22044/jadm.2014.151

来源: DOAJ

【 摘 要 】

The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful tool in signal processing. Unlike traditional time-frequency approaches, HHT exploits the nonlinearity of the medium and non-stationarity of the EEG signals. In addition, we use singular spectrum analysis (SSA) in the pre-processing step as an effective noise removal approach. By using synthetic and real EEG signals, the proposed method is compared with wavelet generalized likelihood ratio (WGLR) as a well-known signal segmentation method. The simulation results indicate the performance superiority of the proposed method.

【 授权许可】

Unknown