Over　recent　decades,　electroencephalogram　(EEG)　has　become　an　essential　tool　in　the　field　of　clinical　analysis　and　neurological　disease　research.　However,　EEG　recordings　are　notably　vulnerable　to　artifacts　during　acquisition,　especially　in　clinical　settings,　which　can　significantly　impede　the　accurate　interpretation　of　neuronal　activity.　Blind　source　separation　is　currently　the　most　popular　method　for　EEG　denoising,　but　most　of　the　sources　it　separates　often　contain　both　artifacts　and　brain　activity,　which　may　lead　to　substantial　information　loss　if　handled　improperly.　In　this　paper,　we　introduce　a　dual-threshold　denoising　method　combining　spatial　filtering　with　frequency-domain　filtering　to　automatically　eliminate　electrooculogram　(EOG)　and　electromyogram　(EMG)　artifacts　from　multi-channel　EEG.　The　proposed　method　employs　a　fusion　of　second-order　blind　identification　(SOBI)　and　canonical　correlation　analysis　(CCA)　to　enhance　source　separation　quality,　followed　by　adaptive　threshold　to　localize　the　artifact　sources,　and　strict　fixed　threshold　to　remove　strong　artifact　sources.　Stationary　wavelet　transform　(SWT)　is　utilized　to　decompose　the　weak　artifact　sources,　with　subsequent　adjustment　of　wavelet　coefficients　in　respective　frequency　bands　tailored　to　the　distinct　characteristics　of　each　artifact.　The　results　of　synthetic　and　real　datasets　show　that　our　proposed　method　maximally　retains　the　time-domain　and　frequency-domain　information　in　the　EEG　during　denoising.　Compared　with　existing　techniques,　the　proposed　method　achieves　better　denoising　performance,　which　establishes　a　reliable　foundation　for　subsequent　clinical　analyses.　IEEE