Objective Intelligent recognition of electroencephalogram (EEG) signals is an important
means for epilepsy detection. Almost all conventional intelligent recognition methods
assume that the training and testing data of EEG signals have identical distribution.
However, this assumption may indeed be invalid for practical applications due to differences
in distributions between the training and testing data, making the conventional epilepsy
detection algorithms not feasible under such situations. In order to overcome this problem …