Super-resolution　(SR)　reconstruction　of　electroencephalography　(EEG)　is　mandatory　in　neuro-science　and　engineering　applications　demanding　fine-grained　spatial　information　while　high-density　EEG　devices　do　not　suit.　The　successes　of　EEG　SR　routinely　rely　on　the　excessive　high-resolution　(HR)　ground　truth　in　order　to　properly　handle　the　spatio-temporal　characteristics　of　EEG,　which　cannot　be　guaranteed　in　the　scenarios　of　long-term　brain　monitoring　due　to　the　insufficient　HR　ground　truth　and　the　subject＇s　individuality.　Aiming　at　this　pitfall,　this　study　proposes　an　ADMM-CMD　approach　(Alternating　Direction　Method　of　Multipliers-　based　Coupled　Matrix　Decomposition),　which　simultaneously　operates　on　the　initial　HR　ground　truth　and　the　low-resolution　(LR)　EEG　requiring　SR　with　the　individual　spatio-temporal　relations　preserved.　First,　the　CMD　model　is　constructed　to　transform　the　initial　HR　ground　truth　and　the　target　LR　EEG　to　the　latent　source　space　with　common　mapping　pattern,　where　functional　connectivity　measure　applies　to　highlight　the　EEG＇s　spatial　characteristics.　Second,　the　ADMM　algorithm　iteratively　solves　the　CMD　model　to　derive　the　mapping　matrix　and　more　importantly　the　latent　sources　embedding　the　temporal　characteristics,　thus　to　support　the　process　of　EEG　SR.　The　experimental　results　on　EEG　datasets　of　Autism　Spectrum　Disorder　(ASD)　&　Typically　Development　(TD)　and　Motor　Imagery　(MI)　indicate　that:　(1)　ADMM-CMD　performs　effectively　in　EEG　SR　reconstruction　with　the　decrease　in　normalized　mean　squared　error　by　0.1%similar　to　7.5%,　the　increase　in　signal-tonoise　ratio　by　up　to　2.1　dB,　and　the　improvement　in　Pearson＇s　correlation　coefficient　by　4.3%,　and　(2)　the　reconstructed　SR　EEG　by　ADMM-CMD　demonstrates　superiority　to　the　LR　alternatives　in　ASD　classification,　especially　when　limited　HR　ground　truth　is　available.