Subject　transfer　is　a　growing　area　of　research　in　EEG　aiming　to　address　the　lack　of　having　enough　EEG　samples　required　for　BCI　by　using　samples　originating　from　individuals　or　groups　of　subjects　that　previously　performed　similar　tasks.　This　paper　investigates　the　feasibility　of　two　frameworks　for　enhancing　subject　transfer　through　a　90%+　reduction　of　EEG　features　and　electrodes　using　Particle　Swarm　Optimization　(PSO).　In　the　first　framework,　electrodes　and　features　selected　by　PSO　from　individual　subjects　are　combined　into　a　single　＂meta-mask＂　to　be　applied　to　the　new　subject.　In　the　second　framework,　the　preprocessed　EEG　of　multiple　subjects　is　concatenated　into　a　single　＂super　subject＂,　from　which　PSO　selects　electrodes　and　features　for　use　on　the　new　subject.　The　study　is　focused　on　finding　the　optimal　mixture　of　subjects　in　either　of　the　proposed　frameworks　in　addition　to　investigating　the　impact　of　various　electrode　and　features　selections.　The　results　indicate　the　important　role　of　having　an　optimal　mixture　of　expertise　in　the　subjects＇　data.　(C)　2013　Elsevier　B.V.　All　rights　reserved.