A Time-Frequency approach for EEG signal segmentation | ||
| Journal of AI and Data Mining | ||
| مقاله 20، دوره 2، شماره 1، خرداد 2014، صفحه 63-71 اصل مقاله (1.13 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2014.151 | ||
| نویسندگان | ||
| Milad Azarbad1؛ Hamed Azami* 2؛ Saeid Sanei3؛ A Ebrahimzadeh4 | ||
| 1Babol University of Technology | ||
| 2Iran University of Science and Technology | ||
| 3Faculty of Engineering and Physical Sciences, University of Surrey | ||
| 4Department of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| EEG signal segmentation؛ time-frequency؛ empirical mode decomposition (EMD)؛ singular spectrum analysis (SSA)؛ Hilbert-Huang transform (HHT) | ||
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