Electroencephalogram　(EEG)　artifact　removal　has　been　investigated　for　decades　with　the　goal　of　reconstructing　the　clean　signals　for　the　subsequent　EEG　analysis.　However,　existing　denoising　methods　still　have　limited　capabilities　to　handle　the　highly　mixed　artifacts　and　the　fine-grained　temporal　dependency　of　artifact-free　EEG　without　a　priori　knowledge　of　the　artifacts.　To　address　the　challenges,　this　study　proposes　a　CNN-Transformer-based　dual-stage　collaborative　ensemble　learning　framework　(namely　CT-DCENet)　in　the　form　of　three　modules:　1)　randomized　collaboration　module　initially　utilizes　four　individual　learners　to　reveal　multi-group　morphological　characteristics　of　the　denoised　EEG,　2)　linear　ensemble　module　integrates　the　outputs　of　four　individual　learners　via　weighted　linear　combination　to　preliminarily　estimate　the　denoised　EEG,　3)　information　complementation　module　takes　in　the　residual　between　the　contaminated　EEG　and　the　above　estimated　EEG,　and　critically　applies　CNN-Transformer-based　feature　extractor　and　denoising　head　to　learn　the　detailed　characteristics　of　the　denoised　EEG.　CT-DCENet　is　conducted　in　a　dual-stage　training　manner　to　derive　the　morphological　characteristics　&　the　detailed　characteristics　of　the　artifact-free　EEG　successively.　The　experimental　results　on　the　public　EEG　datasets　indicate　that　1)　CT-DCENet　significantly　outperforms　the　state-of-the-art　counterparts　(e.g.,　DuoCL,　GCTNet)　under　the　conditions　of　various　artifacts　and　noise　intensities,　where　the　increases　of　SNR　&　PCC　are　0.79　dB,　0.6%　and　the　decrease　of　RRMSE　is　1.9%　for　the　removal　of　EMG,　ECG,　EOG　mixed　artifacts,　2)　the　reconstructed　EEG　by　CT-DCENet　can　well　fit　the　clean　EEG　with　a　low　error　achieved,　especially　for　the　peak　amplitude,　the　high-frequency　area　and　the　boundary　area　of　the　EEG　waveform,　providing　promising　EEG　data　for　the　downstream　task-oriented　EEG　analysis.　　©　2013　IEEE.