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.