a time-frequency approach for eeg signal segmentation

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.