Numerical typing errors can lead to serious consequences, but various causes of human errors and the lack of contextual clues in numerical typing make their prediction difficult.Human behavior modeling can predict the general tendency inmaking errors, while data mining can recognize neurophysiological feedback in detecting cognitive abnormality on a trial-by-trialbasis. This study suggests integrating human behavior modelingand data mining to predict human errors because it utilizes both 1)top-down inference to transform interactions between task characteristics and conditions into a general inclination of an averageoperator to make errors and 2) bottom-up analysis in parsing psychophysiological measurements into an individual’s likelihood ofmaking errors on a trial-by-trial basis. Real-time electroencephalograph (EEG) features collected in a numerical typing experimentand modeling features produced by an enhanced human behaviormodel (queuing network model human processor) were combinedto improve error classification performance by a linear discriminant analysis (LDA) classifier. Integrating EEG and modelingfeatures improved the results of LDA classification by 28.3% inkeenness (d) and by 10.7% in the area under ROC curve (AUC)from that of using EEG only; it also outperformed the other threebenchmarking scenarios: using behaviors only, using apparent taskfeatures, and using task features plus trial information. The AUCwas significantly increased from using EEG along only if EEG +Model features were used.