The　decoding　of　Motor　Imagery　EEG　(MI-EEG)　is　the　most　crucial　part　of　biosignal　processing　in　the　Brain-computer　Interface　(BCI)　system.　The　traditional　recognition　mode　is　always　devoted　to　extracting　and　classifying　the　spatiotemporal　feature　information　of　MI-EEG　in　the　sensor　domain,　but　these　brain　dynamic　characteristics,　which　are　derived　from　the　cerebral　cortical　neurons,　are　reflected　more　immediately　and　obviously　with　high　spatial　resolution　in　the　source　domain.　With　the　development　of　neuroscience,　the　state-of-the-art　EEG　Source　Imaging　(ESI)　technology　converts　the　scalp　signals　into　brain　source　space　and　excavates　the　way　for　source　decoding　of　MI-EEG.　Minimum　Norm　Estimate　(MNE)　is　a　classical　and　original　EEG　inverse　transformation.　Due　to　the　lack　of　depth　weighting　of　dipoles,　it　may　be　more　suitable　for　the　estimation　of　superficial　dipoles　and　will　be　slightly　insufficient　for　further　source　classification.　In　addition,　the　selection　of　a　Region　of　Interest　(ROI)　is　usually　an　essential　step　in　the　source　decoding　of　MI-EEG　by　Independent　Component　Analysis　(ICA),　and　the　most　relevant　independent　component　of　original　EEG　signals　is　transformed　into　the　equivalent　current　dipoles　to　obtain　the　ROI　by　ESI.　Although　the　excellent　results　of　this　method　can　be　obtained　for　unilateral　limb　motor　imaging　EEG　signals,　which　shows　more　distinct　phenomena　of　event-related　desynchronization　(ERD),　the　decoding　accuracy　may　be　restricted　for　more　complex　multi-limb　motor　imagery　tasks,　whose　ERD　is　no　longer　evident.　Therefore,　in　this　paper,　we　propose　a　novel　brain　source　estimation　to　decode　MI-EEG　by　applying　Overlapping　Averaging　(OA)　in　the　temporal　domain　and　Weighted　Minimum　Norm　Estimate　(WMNE),　which　overcomes　the　limitations　of　general　ROI-based　decoding　methods　and　introduces　weighting　factors　to　complement　the　estimation　of　deep　dipoles.　Its　advantages　will　be　evaluated　on　a　public　dataset　with　five　subjects　by　comparing　it　with　MNE,　WMNE,　sLORETA,　OA-MNE　and　ICA-WMNE.　The　proposed　method　reaches　a　higher　average　decoding　accuracy　of　81.32%　compared　to　other　methods　by　10-fold　cross-validation　at　the　same　chance　level.　This　study　will　increase　the　universality　of　the　source　decoding　and　facilitate　the　development　of　a　BCI　system　in　the　source　domain.　(C)　2019　Elsevier　B.V.　All　rights　reserved.