This　study　is　focused　on　the　detection　of　effects　of　features　on　an　infinite　dimensional　response　through　the　conditional　spatial　quantiles　(CSQ)　of　the　response　given　the　features,　and　develops　a　novel　model-free　feature　screening　procedure　for　the　CSQ　regression　function.　Firstly,　a　new　metric　named　kernel-based　conditional　quantile　dependence　(KCQD)　is　proposed　to　measure　the　dependence　of　the　CSQ　on　a　feature.　The　metric　equals　0　if　and　only　if　the　feature　is　independent　of　the　CSQ　of　the　response,　and　thus　is　employed　to　detect　the　contribution　of　a　feature.　Then　a　twostep　feature　screening　procedure　with　the　estimated　KCQD　scores　is　developed　via　a　distributed　strategy.　Theoretical　analyses　reveal　that　the　new　two-step　screening　method　not　only　has　screening　consistency　and　sure　screening　properties　but　also　achieves　control　over　false　discovery　rate　(FDR).　Simulation　studies　show　its　ability　to　control　the　expected　FDR　level　while　maintaining　high　screening　power.　The　proposed　procedure　is　applied　to　analyze　a　magnetoencephalography　dataset,　and　the　identified　signal　positions　are　anatomically　interpretable.