A time-frequency approach for EEG signal segmentation

The record of human brain neural activities, namely electroencephalogram (EEG), is known to be non-stationary in general. In addition, the human head is a non-linear medium for such signals. In many applications, it is useful to divide the EEGs into segments in which the signals can be considered stationary. Here, Hilbert-Huang Transform (HHT), as an effective tool in signal processing is applied since unlike the traditional time-frequency approaches, it exploits the non-linearity of the medium and nonstationarity 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) algorithm as a well-known signal segmentation method. The simulation results indicate the performance superiority of the proposed method.