Establishing　a　high-accuracy　and　training-free　brain-computer　interface　(BCI)　system　is　essential　for　improving　BCI　practicality.　In　this　study,　we　propose　for　the　first　time　a　training-free　double-partial　least-squares　(D-PLS)　model　for　steady-state　visual　evoked　potential　(SSVEP)　detection　that　consists　of　double-layer　PLS,　a　PLS　spatial　filter,　and　a　PLS　feature　extractor.　Electroencephalographic　data　from　11　healthy　volunteers　under　four　different　visual　stimulation　frequencies　were　used　to　test　the　proposed　method.　Compared　with　commonly　used　spatial　filters,　minimum　energy　combination　and　average　maximum　contrast　combination,　the　classification　accuracies　could　be　improved　2-10%　by　our　proposed　PLS　spatial　filter.　Furthermore,　our　proposed　PLS　feature　extractor　achieved　better　performance　than　current　feature　extraction　methods,　namely　power　spectral　density　analysis,　canonical　correlation　analysis,　and　the　use　of　the　least　absolute　shrinkage　and　selection　operator.　The　average　classification　accuracy　for　our　proposed　D-PLS　model　exceeded　90%　when　the　signal　time　window　was　longer　than　3.5　s　and　reached　as　high　as　93.9%　when　the　time　window　was　5　s.　Moreover,　the　D-PLS　model　can　be　easily　set　without　training　data,　so　it　can　be　used　widely　in　SSVEP-based　BCI　systems.