Recently, nonlinear methods that include independent component analysis (ICA) and orthogonal empirical model decomposition (OEMD) have been proposed to extract parameters for the analysis and classification of EEG signals [11, 16]. ICA is a kind of blind source separation technique that extracts statistically independent sources called independent components (ICs) from a set of recorded signals [26]. OEMD is a self-adaptive signal processing and data driven method. Compared with classical time-frequency analysis methods, such as short time Fourier transform (STFT) and Wavelet decomposition, it is based on the local characteristic time scales of a signal and could decompose the signal into a set of complete orthogonal components called intrinsic mode functions (IMFs) which are determined by the signal itself without prior knowledge about the signal [26, 27]. OEMD can overcome the mode aliasing and avoid the occurrence of the fault mode [28].
