The detection of abnormal/unusual events based on dynamically varying spatial data has been of great interest in many real world applications. It is a challenging task to detect abnormal events as they occur rarely and it is very difficult to predict or reconstruct them. Here we address the issue of the detection of propagating phase gradient in the sequence of brain images obtained by EEG arrays. We compare two alternative methods of abnormal event detection. One is based on prediction using a linear dynamical system, while the other is a model-based algorithm using expectation minimization approach. The comparison identifies the pros and cons of the different methods, moreover it helps to develop an integrated and robust algorithm for monitoring cognitive behaviors, with potential applications including brain-computer interfaces (BCI).