A　practical　strategy　for　classifying　the　lying　electroencephalograph　(EEG)　characters　by　wavelet　decomposition　and　support　vector　machines(SVM)　techniques　is　presented　to　get　a　satisfactory　results　in　identifying　the　mentality　facticity.　Some　significant　personal　information　is　ensured,　such　as　name　and　birthday,　and　selected　as　the　concealed　information.　15　subjects　participate　in　two　groups　of　concealed　information　tests　(CIT)　and　their　EEGs　are　recorded.　Applying　wavelet　decomposition,　the　wavelet　coefficients　corresponding　to　EEG　evoked　by　probe　information　and　by　irrelevant　information　respectively　are　evaluated.　Then　the　feature　coefficients　containing　statistical　significance　are　extracted　as　the　input　parameters　of　SVM.　30　samples　are　chosen　to　train　and　test　the　performance　of　classifier　by　leave-one-out　cross-validation,　88.3%　accuracy　can　be　achieved　in　probe　information　detection.