Automatic　detection　and　classification　of　electroencephalogram　(EEG)　epileptic　activity　aid　diagnosis　and　relieve　the　heavy　workload　of　doctors.　This　article　presents　a　new　EEG　classification　approach　based　on　the　extreme　learning　machine　(ELM)　and　wavelet　transform　(WT).　First,　the　WT　is　used　to　extract　useful　features　when　certain　scales　cover　abnormal　components　of　the　EEG.　Second,　the　ELM　algorithm　is　used　to　train　a　single　hidden　layer　of　feedforward　neural　network　(SLFN)　features.　Finally,　the　SLFN　is　tested　with　interictal　and　ictal　EEGs.　The　experiments　demonstrated　that　the　proposed　approach　achieved　a　satisfactory　classification　rate　of　99.25%　for　interictal　and　ictal　EEGs.