This　paper　aims　to　solve　automated　feature　selection　problem　in　brain　computer　interface　(BCI).　In　order　to　automate　feature　selection　process,　we　proposed　a　novel　EEG　feature　selection　method　based　on　decision　tree　(DT).　During　the　electroencephalogram　(EEG)　signal　processing,　a　feature　extraction　method　based　on　principle　component　analysis　(PCA)　was　used,　and　the　selection　process　based　on　decision　tree　was　performed　by　searching　the　feature　space　and　automatically　selecting　optimal　features.　Considering　that　EEG　signals　are　a　series　of　non-linear　signals,　a　generalized　linear　classifier　named　support　vector　machine　(SVM)　was　chosen.　In　order　to　test　the　validity　of　the　proposed　method,　we　applied　the　EEG　feature　selection　method　based　on　decision　tree　to　BCI　Competition　II　datasets　Ia,　and　the　experiment　showed　encouraging　results.