EEG　recording　is　a　time　consuming　operation　during　which　the　subject　is　expected　to　stay　still　for　a　long　time　performing　tasks.　It　is　reasonable　to　expect　some　fluctuation　in　the　level　of　focus　toward　the　performed　task　during　the　task　period.　This　study　is　focused　on　investigating　various　approaches　for　emphasizing　regions　of　interest　during　the　task　period.　Dividing　the　task　period　into　three　segments　of　beginning,　middle　and　end,　is　expectable　to　improve　the　overall　classification　performance　by　changing　the　concentration　of　the　training　samples　toward　regions　in　which　subject　had　better　concentration　toward　the　performed　tasks.　This　issue　is　investigated　through　the　use　of　techniques　such　as　i)　replication,　ii)　biasing,　and　iii)　overlapping.　A　dataset　with　4　motor　imagery　tasks　(BCI　Competition　III　dataset　IIIa)　is　used.　The　results　illustrate　the　existing　variations　within　the　potential　of　different　segments　of　the　task　period　and　the　feasibility　of　techniques　that　focus　the　training　samples　toward　such　regions.　©　2012　Springer-Verlag.