Brain-computer　interfaces　(BCIs)　based　on　motor　imagery　(MI)　utilizing　multi-channel　electroencephalogram　(EEG)　data　are　commonly　used　to　improve　motor　function　of　people　with　motor　disabilities.　EEG　channel　selection　can　enhance　MI　classification　accuracy　by　selecting　informative　channels,　accordingly　reducing　redundant　information.　The　sequential　backward　floating　search　(SBFS)　approach　has　been　considered　as　one　of　the　best　feature　selection　methods.　In　this　paper,　SBFS　is　first　implemented　to　select　the　optimal　EEG　channels　in　MI-BCI.　Further,　to　reduce　the　time　complexity　of　SBFS,　the　modified　SBFS　is　proposed　and　applied　to　left　and　right　hand　MI　tasks.　In　the　modified　SBFS,　based　on　the　map　of　EEG　channels　at　the　scalp,　the　symmetrical　channels　are　selected　as　channel　pairs　and　acceleration　is　thus　realized　by　removing　or　adding　multiple　channels　in　each　iteration.　Extensive　experiments　were　conducted　on　four　public　BCI　datasets.　Experimental　results　show　that　the　SBFS　achieves　significantly　higher　classification　accuracy　(p　＜　0.001)　than　using　all　channels　and　conventional　MI　channels　(i.e.,　C3,　C4,　and　Cz).　Moreover,　the　proposed　method　outperforms　the　state-of-the-art　selection　methods.　Copyright　©　2022　Tang,　Gao,　Li　and　Chen.