Motor　Imagery-based　Brain　Computer　Interface　(MI-BCI)　is　a　typical　active　BCI　with　a　main　focus　on　motor　intention　identification.　Hybrid　motor　imagery　(MI)　decoding　methods　that　based　on　multi-modal　fusion　of　Electroencephalography　(EEG)　and　functional　near-infrared　spectroscopy　(fNIRS),　especially　deep　learning-based　methods,　become　popular　in　recent　MI-BCI　studies.　However,　the　fusion　strategy　and　network　design　in　deep　learning-based　methods　are　complex.　To　solve　this　problem,　we　proposed　the　multi-channel　fusion　method　(MCF)　to　simplify　current　fusion　methods,　and　we　designed　a　multi-channel　fusion　hybrid　network　(MCFHNet)　based　on　MCF.　MCFHNet　combines　depthwise　convolutional　layers,　channel　attention　mechanism,　and　Bidirectional　Long　Short　Term　Memory　(Bi-LSTM)　layers,　which　enables　strong　capability　of　feature　extraction　in　spatial　and　temporal　domain.　The　comparison　between　MCFHNet　and　representative　deep　learning-based　methods　was　performed　on　an　open　EEG-fNIRS　dataset.　We　found　the　proposed　method　can　yield　superior　performance　(mean　accuracy　of　99.641　%　in　5-fold　cross　validation　of　an　intra-subject　experiment).　This　work　provides　a　new　option　for　multi-modal　MI　decoding,　which　can　be　applied　in　the　rehabilitation　field　based　on　hybrid　BCI　systems.　©　2022　IEEE.