Electroencephalography　(EEG)　is　a　widely　used　tool　for　monitoring　brain　activity,　but　it　is　often　disturbed　by　various　artifacts,　such　as　electrooculography　(EOG),　electromyography　(EMG),　and　electrocardiography　(ECG),　which　degrade　signal　quality　and　affect　subsequent　analysis.　Effective　EEG　denoising　is　critical　for　enhancing　the　performance　of　EEG-based　applications,　including　disease　diagnosis　and　brain-computer　interfaces　(BCIs).　While　recent　deep　learning　(DL)　approaches　have　shown　promise　in　this　area,　they　often　struggle　to　efficiently　model　the　temporal　dependencies　inherent　in　EEG　signals,　as　well　as　to　capture　local　contextual　information　simultaneously.　In　this　work,　we　introduce　DenoiseMamba,　a　novel　deep　learning-based　EEG　denoising　model.　The　model　incorporates　the　ConvSSD　module,　which　integrates　convolutional　neural　networks　(CNNs)　with　structured　state-space　duality　(SSD)　mechanisms.　This　allows　DenoiseMamba　to　capture　both　local　and　global　spatiotemporal　features,　resulting　in　more　effective　artifact　suppression.　Extensive　experiments　on　three　semi-simulated　datasets　demonstrate　that　DenoiseMamba　outperforms　existing　methods　in　EEG　reconstruction　accuracy,　effectively　eliminating　myoelectric,　electrooculographic,　and　electrocardiographic　artifacts　while　preserving　critical　EEG　signal　details.　©　2013　IEEE.