A　motor　imagery　EEG　(MI-EEG)　signal　is　often　selected　as　the　driving　signal　in　an　active　brain　computer　interface　(BCI)　system,　and　it　has　been　a　popular　field　to　recognize　MI-EEG　images　via　convolutional　neural　network　(CNN),　which　poses　a　potential　problem　for　maintaining　the　integrity　of　the　time-frequency-space　information　in　MI-EEG　images　and　exploring　the　feature　fusion　mechanism　in　the　CNN.　However,　information　is　excessively　compressed　in　the　present　MI-EEG　image,　and　the　sequential　CNN　is　unfavorable　for　the　comprehensive　utilization　of　local　features.　In　this　paper,　a　multidimensional　MI-EEG　imaging　method　is　proposed,　which　is　based　on　time-frequency　analysis　and　the　Clough-Tocher　(CT)　interpolation　algorithm.　The　time-frequency　matrix　of　each　electrode　is　generated　via　continuous　wavelet　transform　(WT),　and　the　relevant　section　of　frequency　is　extracted　and　divided　into　nine　submatrices,　the　longitudinal　sums　and　lengths　of　which　are　calculated　along　the　directions　of　frequency　and　time　successively　to　produce　a　3　x　3　feature　matrix　for　each　electrode.　Then,　feature　matrix　of　each　electrode　is　interpolated　to　coincide　with　their　corresponding　coordinates,　thereby　yielding　a　WT-based　multidimensional　image,　called　WTMI.　Meanwhile,　a　multilevel　and　multiscale　feature　fusion　convolutional　neural　network　(MLMSFFCNN)　is　designed　for　WTMI,　which　has　dense　information,　low　signal-to-noise　ratio,　and　strong　spatial　distribution.　Extensive　experiments　are　conducted　on　the　BCI　Competition　IV　2a　and　2b　datasets,　and　accuracies　of　92.95%　and　97.03%　are　yielded　based　on　10-fold　cross-validation,　respectively,　which　exceed　those　of　the　state-of-the-art　imaging　methods.　The　kappa　values　and　p　values　demonstrate　that　our　method　has　lower　class　skew　and　error　costs.　The　experimental　results　demonstrate　that　WTMI　can　fully　represent　the　time-frequency-space　features　of　MI-EEG　and　that　MLMSFFCNN　is　beneficial　for　improving　the　collection　of　multiscale　features　and　the　fusion　recognition　of　general　and　abstract　features　for　WTMI.