Feature　extraction　plays　an　important　role　in　brain　computer　interface　system　that　significantly　affects　the　success　of　brain　signal　classification.　In　this　paper,　a　feature　extraction　method　of　electroencephalographic　(EEG)　signals　based　on　Extreme　Learning　Machine　auto-encoder　(ELM-AE)　is　applied.　Firstly,　the　original　data　is　classified　by　Extreme　Learning　Machine　(ELM)　and　the　number　of　hidden　layer＇s　neuron　with　the　highest　accuracy　is　selected　as　the　dimension　of　feature　extraction.　Then,　ELM-AE＇s　output　weight　learns　to　represent　the　features　of　the　original　data.　Finally,　the　features　are　classified　by　Support　Vector　Machine　(SVM)　classifier.　Experiment　result　shows　the　efficiency　of　our　method　for　both　the　speed　of　feature　extraction　and　the　accuracy　of　the　classification　for　data　set　la,　which　is　a　typical　representative　of　one　kind　of　BCI　competition　2003　data.