In　this　study,　an　EEG　signal　classification　framework　was　proposed.　The　framework　contained　three　feature　extraction　methods　refer　to　optimization　strategy.　Firstly,　we　selected　optimal　electrodes　based　on　the　single　electrode　classification　performance　and　combined　all　the　optimal　electrodes＇　data　as　the　feature.　Then,　we　discussed　the　contribution　of　each　time　span　of　EEG　signals　for　each　electrode　and　joined　all　the　optimal　time　spans＇　data　together　to　be　used　for　classifying.　In　addition,　wefurther　selected　useful　information　from　original　data　based　on　genetic　algorithm.　Finally,　the　performances　were　evaluated　by　Bayes　and　SVM　classifiers　on　BCI　2003　Competition　data　set　Ia.　And　the　accuracy　of　genetic　algorithm　has　reached　91.81%.　The　experimental　results　show　that　our　methods　offer　the　better　performance　for　reliable　classification　of　the　EEG　signal.　©　Maxwell　Scientific　Organization,　2013.