This　paper　presents　an　approach　to　classifying　electroencephalogram　(EEG)　signals　for　brain-computer　interfaces　(BCI).　To　eliminate　redundancy　in　high-dimensional　EEG　signals　and　reduce　the　coupling　among　different　classes　of　EEG　signals,　we　use　principle　component　analysis　and　linear　discriminant　analysis　to　extract　features　that　represent　the　raw　signals.　Next,　we　introduce　the　voting-based　extreme　learning　machine　to　classify　the　features.　Experiments　performed　on　real-world　data　from　the　2003　BCI　competition　indicate　that　our　classification　method　outperforms　state-of-the-art　methods　in　speed　and　accuracy.