Objective.　Motor　Imagery　Brain-Computer　Interface　(MI-BCI)　is　an　active　Brain-Computer　Interface　(BCI)　paradigm　focusing　on　the　identification　of　motor　intention,　which　is　one　of　the　most　important　non-invasive　BCI　paradigms.　In　MI-BCI　studies,　deep　learning-based　methods　(especially　lightweight　networks)　have　attracted　more　attention　in　recent　years,　but　the　decoding　performance　still　needs　further　improving.　Approach.　To　solve　this　problem,　we　designed　a　filter　bank　structure　with　sinc-convolutional　layers　for　spatio-temporal　feature　extraction　of　MI-electroencephalography　in　four　motor　rhythms.　The　Channel　Self-Attention　method　was　introduced　for　feature　selection　based　on　both　global　and　local　information,　so　as　to　build　a　model　called　Filter　Bank　Sinc-convolutional　Network　with　Channel　Self-Attention　for　high　performance　MI-decoding.　Also,　we　proposed　a　data　augmentation　method　based　on　multivariate　empirical　mode　decomposition　to　improve　the　generalization　capability　of　the　model.　Main　results.　We　performed　an　intra-subject　evaluation　experiment　on　unseen　data　of　three　open　MI　datasets.　The　proposed　method　achieved　mean　accuracy　of　78.20%　(4-class　scenario)　on　BCI　Competition　IV　IIa,　87.34%　(2-class　scenario)　on　BCI　Competition　IV　IIb,　and　72.03%　(2-class　scenario)　on　Open　Brain　Machine　Interface　(OpenBMI)　dataset,　which　are　significantly　higher　than　those　of　compared　deep　learning-based　methods　by　at　least　3.05%　(p　=　0.0469),　3.18%　(p　=　0.0371),　and　2.27%　(p　=　0.0024)　respectively.　Significance.　This　work　provides　a　new　option　for　deep　learning-based　MI　decoding,　which　can　be　employed　for　building　BCI　systems　for　motor　rehabilitation.