Motor　imagery-based　brain　computer　interface　(MI-BCI)　is　a　representative　active　BCI　paradigm　which　is　widely　employed　in　the　rehabilitation　field.　In　MI-BCI,　a　classification　model　is　built　to　identify　the　target　limb　from　MI-based　EEG　signals,　but　the　performance　of　models　cannot　meet　the　demand　for　practical　use.　Lightweight　neural　networks　in　deep　learning　methods　are　used　to　build　high　performance　models　in　MI-BCI.　Small　sample　sizes　and　the　lack　of　multi-scale　information　extraction　in　frequency　domain　limit　the　performance　improvement　of　lightweight　neural　networks.　To　solve　these　problems,　the　Filter　Bank　Sinc-ShallowNet　(FB-Sinc-ShallowNet)　algorithm　combined　with　the　mixed　noise　adding　method　based　on　empirical　mode　decomposition　(EMD)　was　proposed.　The　FB-Sinc-ShallowNet　algorithm　improves　a　lightweight　neural　network　Sinc-ShallowNet　with　a　filter　bank　structure　corresponding　to　four　sensory　motor　rhythms.　The　mixed　noise　adding　method　employs　the　EMD　method　to　improve　the　quality　of　generated　data.　The　proposed　method　was　evaluated　on　the　BCI　competition　IV　IIa　dataset　and　can　achieve　highest　average　accuracy　of　77.2%,　about　634%　higher　than　state-of-the-art　method　Sinc-ShallowNet.　This　work　implies　the　effectiveness　of　filter　bank　structure　in　lightweight　neural　networks　and　provides　a　novel　option　for　data　augmentation　and　classification　of　MI-based　EEG　signals,　which　can　be　applied　in　the　rehabilitation　field　for　decoding　MI-EEG　with　few　samples.