The difficulty in developing a classification system based on EEG is to discriminate the responses from the background noise reliably because the signals are relatively weak and interfered easily by the artifacts, such as electromagnetic interference, powerline noise, EOG, EMG, ECG, and subject movements. Some preprocessing algorithms have been applied to the EEG data in order to extract more informative features, which can be used as inputs to a classifier to improve the classification accuracy of task-related activity, such as ICA and OEMD [32, 33]. The EMD method may encounter the difficulty of mode mixing, and it is mainly caused by noise, boundary effect, and so forth. This type of mixing will lead to the presence of several components of the signal of interest on the same IMF, which can cause the difficulty in the physical discriminant of each mode. To solve these problems, ICA and OEMD were combined in this study. we first removed the artifacts from the given ERP signals to extract statistically independent sources by independent component analysis (ICA) and then decomposed them to extract real IMF components using OEMD algorithm.
