In　the　BCI　rehabilitation　system,　the　decoding　of　motor　imagery　tasks　(MI-tasks)　with　dipoles　in　the　source　domain　has　gradually　become　a　new　research　focus.　For　complex　multiclass　MI-tasks,　the　number　of　activated　dipoles　is　large,　and　the　activation　area,　activation　time　and　intensity　are　also　different　for　different　subjects.　The　means　by　which　to　identify　fewer　subject-based　dipoles　is　very　important.　There　exist　two　main　methods　of　dipole　selection:　one　method　is　based　on　the　physiological　functional　partition　theory,　and　the　other　method　is　based　on　human　experience.　However,　the　number　of　dipoles　that　are　selected　by　the　two　methods　is　still　large　and　contains　information　redundancy,　and　the　selected　dipoles　are　the　same　in　both　number　and　position　for　different　subjects,　which　is　not　necessarily　ideal　for　distinguishing　different　MI-tasks.　In　this　paper,　the　data-driven　method　is　used　to　preliminarily　select　fully　activated　dipoles　with　large　amplitudes;　the　obtained　dipoles　are　refined　by　using　continuous　wavelet　transform　(CWT)　to　best　reflect　the　differences　among　the　multiclass　MI-tasks,　thereby　yielding　a　subject-based　dipole　selection　method,　which　is　named　PRDS.　PRDS　is　further　used　to　decode　multiclass　MI-tasks　in　which　some　representative　dipoles　are　found,　and　their　wavelet　coefficient　power　is　calculated　and　input　to　one-vs.-one　common　spatial　pattern　(OVO-CSP)　for　feature　extraction,　and　the　features　are　classified　by　the　support　vector　machine.　We　denote　this　decoding　method　as　D-CWTCSP,　which　enhances　the　spatial　resolution　and　also　makes　full　use　of　the　time-frequency-spatial　domain　information.　Experiments　are　carried　out　using　a　public　dataset　with　nine　subjects　and　four　classes　of　MI-tasks,　and　the　proposed　D-CWTCSP　is　compared　with　the　relevant　methods　in　sensor　space　and　brain-source　space　in　terms　of　the　decoding　accuracy,　standard　deviation,　recall　rate　and　kappa　value.　The　experimental　results　show　that　D-CWTCSP　reaches　an　average　decoding　accuracy　of　82.66%　among　the　nine　subjects,　which　generates　8-20%　improvement　over　other　methods,　thus　reflecting　its　great　superiority　in　decoding　accuracy.　(C)　2020　Elsevier　B.V.　All　rights　reserved.