Electroencephalography　(EEG)　based　Brain-Computer　Interface　(BCI)　enables　subjects　to　communicate　with　the　outside　world　or　control　equipment　using　brain　signals　without　passing　through　muscles　and　nerves.　Many　researchers　in　recent　years　have　studied　the　non-invasive　BCI　systems.　However,　the　efficiency　of　the　intention　decoding　algorithm　is　affected　by　the　random　non-stationary　and　low　signal-to-noise　ratio　characteristics　of　the　EEG　signal.　Furthermore,　channel　selection　is　another　important　issue　in　BCI　systems　intention　recognition.　During　intention　recognition　in　BCI　systems,　the　unnecessary　information　produced　by　redundant　electrodes　affects　the　decoding　rate　and　deplete　system　resources.　In　this　paper,　we　introduce　a　recurrent-convolution　neural　network　model　for　intention　recognition　by　learning　decomposed　spatio-temporal　representations.　We　apply　the　novel　Gradient-Class　Activation　Mapping　(Grad-CAM)　visualization　technology　to　the　channel　selection.　Grad-CAM　uses　the　gradient　of　any　classification,　flowing　into　the　last　convolutional　layer　to　produce　a　coarse　localization　map.　Since　the　pixels　of　the　localization　map　correspond　to　the　spatial　regions　where　the　electrodes　are　placed,　we　select　the　channels　that　are　more　important　for　decision-making.　We　conduct　an　experiment　using　the　public　motor　imagery　EEG　dataset　EEGMMIDB.　The　experimental　results　demonstrate　that　our　method　achieves　an　accuracy　of　97.36%　at　the　full　channel,　outperforming　many　state-of-the-art　models　and　baseline　models.　Although　the　decoding　rate　of　our　model　is　the　same　as　the　best　model　compared,　our　model　has　fewer　parameters　with　faster　training　time.　After　the　channel　selection,　our　model　maintains　the　intention　decoding　performance　of　92.31%　while　reducing　the　number　of　channels　by　nearly　half　and　saving　system　resources.　Our　method　achieves　an　optimal　trade-off　between　performance　and　the　number　of　electrode　channels　for　EEG　intention　decoding.　©　2020　Elsevier　B.V.