Training　Brain　Computer　Interface　(BCI)　systems　to　understand　the　intention　of　a　subject　through　Electroencephalogram　(EEG)　data　currently　requires　multiple　training　sessions　with　a　subject　in　order　to　develop　the　necessary　expertise　to　distinguish　signals　for　different　tasks.　Conventionally　the　task　of　training　the　subject　is　done　by　introducing　a　training　and　calibration　stage　during　which　some　feedback　is　presented　to　the　subject.　This　training　session　can　take　several　hours　which　is　not　appropriate　for　on-line　EEG-based　BCI　systems.　An　alternative　approach　is　to　use　previous　recording　sessions　of　the　same　person　or　some　other　subjects　that　performed　the　same　tasks　(subject　transfer)　for　training　the　classifiers.　The　main　aim　of　this　study　is　to　generate　a　methodology　that　allows　the　use　of　data　from　other　subjects　while　reducing　the　dimensions　of　the　data.　The　study　investigates　several　possibilities　for　reducing　the　necessary　training　and　calibration　period　in　subjects　and　the　classifiers　and　addresses　the　impact　of　i)　evolutionary　subject　transfer　and　adapting　previously　trained　methods　(retraining)　using　other　subjects　data.　Our　results　suggest　reduction　to　40%　of　target　subject　data　is　suffidient　for　training　the　classifier.　Our　results　also　indicate　the　superiority　of　the　approaches　that　incorporated　evolutionary　subject　transfer　and　highlights　the　feasibility　of　adapting　a　system　trained　on　other　subjects.