As　connections　from　the　brain　to　an　external　device,　Brain-Computer　Interface　(BCI)　systems　are　a　crucial　aspect　of　assisted　communication　and　control.　When　equipped　with　well-designed　feature　extraction　and　classification　approaches,　information　can　be　accurately　acquired　from　the　brain　using　such　systems.　The　Hierarchical　Extreme　Learning　Machine　(HELM)　has　been　developed　as　an　effective　and　accurate　classification　approach　due　to　its　deep　structure　and　extreme　learning　mechanism.　A　classification　system　for　motor　imagery　EEG　signals　is　proposed　based　on　the　HELM　combined　with　a　kernel,　herein　called　the　Kernel　Hierarchical　Extreme　Learning　Machine　(KHELM).　Principle　Component　Analysis　(PCA)　is　used　to　reduce　the　dimensionality　of　the　data,　and　Linear　Discriminant　Analysis　(LDA)　is　introduced　to　push　the　features　away　from　different　classes.　To　demonstrate　the　performance,　the　proposed　system　is　applied　to　the　BCI　competition　2003　Dataset　Ia,　and　the　results　are　compared　with　those　from　state-of-the-art　methods;　we　find　that　the　accuracy　is　up　to　94.54%.