Motor　imagery　electroencephalograph　(MI-EEG)　classification　plays　an　important　role　in　noninvasive　braincomputer　interfaces　(BCIs).　However,　the　distribution　shifts　among　different　subjects　make　a　major　challenge　to　build　classification　models.　Due　to　temporally-varying　and　spatially-coupling　characteristics　of　MI-EEG　data,　recent　methods　have　suffered　from　incomplete　feature　representations　and　the　accumulation　of　incorrect　pseudolabels,　even　lower　efficiency.　To　address　these　issues,　the　paper　proposes　a　novel　method　for　cross-subject　MIEEG　classification,　namely　Joint　spatial　Feature　Adaptation　and　Confident　Pseudo-label　Selection　(JFACPS).　JFACPS　extracts　joint　spatial　feature　representations　from　two　perspectives　upon　the　aligned　MI-EEG　samples,　where　the　spatio-temporal　filtering　features　are　extracted　upon　Euclidean　space　and　the　tangent　space　mapping　features　are　extracted　upon　Riemannian　space.　Then,　the　joint　spatial　features　are　incorporated　into　a　discriminative　pseudo-labeling　framework　for　feature　adaptation.　Among　them,　the　samples　with　large　differences　in　confidence　between　the　highest　and　second-highest　predictions　are　selected　for　adaptation.　Meanwhile,　a　novel　classifier　is　introduced　to　initialize　more　accurate　pseudo-labels　with　high　confidence　during　the　first　iteration　of　feature　adaptation.　We　systematically　conducted　the　experiments　on　two　benchmark　MI-EEG　datasets,　and　the　classification　performance　of　JFACPS　surpasses　several　state-of-the-art　methods.　Moreover,　ablation　studies　also　demonstrated　the　significance　for　both　joint　spatial　feature　and　confident　pseudo-label　selection.　Based　on　the　parameter　insensitivity　experiments,　our　JFACPS　method　provides　a　novel　calibration　option　for　new　subjects　participating　in　MI-BCIs.