Brain　-computer　interface　(BCI)　is　an　emerging　technology　which　provides　a　road　to　control　communication　and　external　devices.　Electroencephalogram　(EEG)　-based　motor　imagery　(MI)　tasks　recognition　has　important　research　significance　for　stroke,　disability　and　others　in　BCI　fields.　However,　enhancing　the　classification　performance　for　decoding　MI　-related　EEG　signals　presents　a　significant　challenge,　primarily　due　to　the　variability　across　different　subjects　and　the　presence　of　irrelevant　channels.　To　address　this　issue,　a　novel　hybrid　structure　is　developed　in　this　study　to　classify　the　MI　tasks　via　deep　separable　convolution　network　(DSCNN)　and　bidirectional　long　short-term　memory　(BLSTM).　First,　the　collected　time　-series　EEG　signals　are　initially　processed　into　a　matrix　grid.　Subsequently,　data　segments　formed　using　a　sliding　window　strategy　are　inputted　into　proposed　DSCNN　model　for　feature　extraction　(FE)　across　various　dimensions.　And,　the　spatial　-temporal　features　extracted　are　then　fed　into　the　BLSTM　network,　which　further　refines　vital　time　-series　features　to　identify　five　distinct　types　of　MI　-related　tasks.　Ultimately,　the　evaluation　results　of　our　method　demonstrate　that　the　developed　model　achieves　a　98.09%　accuracy　rate　on　the　EEGMMIDB　physiological　datasets　over　a　4　-second　period　for　MI　tasks　by　adopting　full　channels,　outperforming　other　existing　studies.　Besides,　the　results　of　the　five　evaluation　indexes　of　Recall,　Precision,　Test-auc,　and　F1　-score　also　achieve　97.76%,　97.98%,　98.63%　and　97.86%,　respectively.　Moreover,　a　Gradient　-class　Activation　Mapping　(GRAD　-CAM)　visualization　technique　is　adopted　to　select　the　vital　EEG　channels　and　reduce　the　irrelevant　information.　As　a　result,　we　also　obtained　a　satisfying　outcome　of　94.52%　accuracy　with　36　channels　selected　using　the　Grad　-CAM　approach.　Our　study　not　only　provides　an　optimal　trade-off　between　recognition　rate　and　number　of　channels　with　half　the　number　of　channels　reduced,　but　also　it　can　also　advances　practical　application　research　in　the　field　of　BCI　rehabilitation　medicine,　effectively.