The　selection　of　time　segment　and　frequency　band　always　play　a　vital　role　in　the　decoding　of　Motor　Imagery　Tasks　(MI-tasks),　especially　for　the　feature　extraction　of　MI-Electroencephalographic　(MI-EEG).　The　excavation　of　valuable　and　discriminative　feature　information　needs　to　be　based　on　the　reliable　time-frequency　analysis,　which　is　the　foremost　precondition　for　feature　engineering.　However,　relying　on　the　high　temporal　resolution　of　MI-EEG,　traditional　feature　extraction　methods　can　only　conduct　the　time-frequency　analysis　according　to　the　superficial　neurophysiological　rhythm　of　EEG　in　the　sensor　domain.　And　more　detailed　time-frequency　characteristics　could　hardly　be　embodied　in　a　few　channels　of　MI-EEG　signals,　which　leads　to　a　coarse　selection　of　time-frequency　interval　and　the　resulted　lower　decoding　effect.　Therefore,　a　neurophysiology-based　technique　is　needed　for　performing　more　exact　time-frequency　analysis.　Based　on　the　advanced　EEG　Source　Imaging,　a　Wrapped　Time-Frequency　combined　Selection　in　the　Source　Domain,　which　is　denoted　as　WTFS-SD,　is　proposed　for　decoding　the　MI-tasks　by　applying　Weighted　Minimum　Norm　Estimate　and　CSP　based　sub-band　feature　extraction　in　this　paper.　Abundant　comparative　experiments　are　conducted　on　the　BCI2000　system　dataset　with　six　subjects,　and　the　results　show　that　the　proposed　methods　can　select　subject-specific　optimal　frequency　band　and　TOI,　which　yields　the　highest　average　classification　rate　of　93.14%　by　9-fold　cross-validation　at　the　same　chance　level　as　well　as　a　superior　mean　kappa　coefficient　of　0.8627　across　all　subjects　compared　to　other　prevalent　methods.　This　study　will　enhance　the　decoding　of　complex　MI-tasks　and　be　helpful　for　the　development　of　intelligent　BCI　system.　(C)　2019　Elsevier　Ltd.　All　rights　reserved.