Error-related potential (ErrP) usually emerges in the brain when human perceives errors and is believed to be a promising signal for optimizing brain-computer interface (BCI) system. However, most of the ErrP studies only focus on how to distinguish the correct and wrong conditions, which is not enough for the BCI application in real scenarios. Therefore, it is necessary to study the ErrPs induced by the prediction deviants with varying degrees, concurrently test the separability of such EEG features. To this end, electroencephalogram (EEG) data of twelve healthy subjects were recorded when they participated in a direction prediction experiment. There are three prediction -deviant conditions in it, i.e., correct prediction, 90°deviant, 180° deviant. Event-related potential and inter-trial coherence were analyzed. Consequently, the error-related negativity (ERN) and N450 component in FCZ were significantly modulated by the degrees of prediction deviants, especially in the low-frequency band (<13Hz). Moreover, single-trial classification was adopted to test the separability of these features; the averaged accuracies between any two conditions were 87.75%, 85.25%, 64.79%. This study demonstrates the prediction deviants with varying degrees can induce separable ErrP features, which provide a deeper understanding of the ErrP signatures for developing BCIs.