Epileptic　seizure　detection　using　multi-channel　scalp　electroencephalogram　(EEG)　signals　has　gained　increasing　attention　in　clinical　therapy.　Recently,　researchers　attempt　to　employ　deep　learning　techniques　with　channel　selection　to　determine　critical　channels.　However,　existing　models　with　such　hard　selection　procedure　do　not　take　dynamic　constraints　into　account,　since　the　irrelevant　channels　vary　significantly　across　different　situations.　To　address　these　issues,　we　propose　ChannelAtt,　an　end-to-end　multi-view　deep　learning　model　with　channel-aware　attention　mechanism,　to　express　multi-channel　EEG　signals　in　a　high-level　space　with　interpretable　meanings.　ChannelAtt　jointly　learns　both　multi-view　representation　and　its　contribution　scores.　We　propose　two　attention　mechanisms　to　learn　the　attentional　representations　of　multi-channel　EEG　signals　in　time-frequency　domain.　Experimental　results　show　that　the　proposed　ChannelAtt　model　outperforms　the　baselines　in　detecting　epileptic　seizures.　Analytical　results　of　a　case　study　demonstrate　that　the　learned　attentional　representations　are　meaningful.　©　2018　IEEE.