Brain-computer　interface　(BCI)　is　a　modern　useful　tool　of　bypassing　usual　channels　of　muscle　and　peripheral　nervous　system　to　establish　a　direct　connection　between　brain　and　external　devices　and　to　restore　fundamental　communication　and　control　skills.　Steady-state　visual　evoked　potential　(SSVEP),　as　one　of　the　most　popular　EEG　modality,　has　been　widely　used　in　BCI　applications.　For　SSVEP　BCI,　the　most　challenging　task　is　to　effectively　improve　the　accuracy,　especially　in　minimum　number　of　recording　electrodes　and　short　stimulation　duration.　In　this　study,　a　novel　multi-channel　integrated　GT(circ)(2)　statistic　method　was　proposed　for　the　frequency　recognition　in　a　four-class　steady-state　motion　visual　evoked　potential　(SSMVEP)-based　BCI.　The　proposed　method　was　compared　with　the　widely　used　canonical　correlation　analysis　(CCA)　and　verified　with　three-channel　EEG　data　from　three　healthy　subjects.　Results　indicated　that　a　higher　recognition　performance　with　shorter　recording　time　and　few　electrodes　can　be　achieved　by　using　of　this　novel　method　rather　than　CCA　method,　making　multi-channel　integrated　GT(circ)(2)　statistic　a　robust　approach　for　the　implementation　of　SSVEP　BCIs.