The　high　dimensional　nature　of　EEG　data　due　to　large　electrode　numbers　and　long　task　periods　is　one　of　the　main　challenges　of　studying　EEG.　Evolutionary　alternatives　to　conventional　dimension　reduction　methods　exhibit　the　advantage　of　not　requiring　the　entire　recording　sessions　for　operation.　Particle　Swarm　Optimization　(PSO)　is　an　Evolutionary　method　that　achieves　performance　through　evaluation　of　several　generations　of　possible　solutions.　This　study　investigates　the　feasibility　of　a　2　layer　PSO　structure　for　synchronous　reduction　of　both　electrode　and　task　period　dimensions　using　4　motor　imagery　EEG　data.　The　results　indicate　the　potential　of　the　proposed　PSO　paradigm　for　dimension　reduction　with　insignificant　losses　in　classification　and　the　practical　uses　in　subject　transfer　applications.　©　2012　Springer-Verlag.