Artifact　removal　has　been　an　open　critical　is-sue　for　decades　in　tasks　centering　on　EEG　analysis.　Re-cent　deep　learning　methods　mark　a　leap　forward　from　the　conventional　signal　processing　routines;　however,　those　in　general　still　suffer　from　insufficient　capabilities　1)　to　capture　potential　temporal　dependencies　embedded　in　EEG　and　2)　to　adapt　to　scenarios　without　a　priori　knowledge　of　artifacts.　This　study　proposes　an　approach　(namely　DuoCL)　to　deep　artifact　removal　with　a　dual-scale　CNN　(Convolutional　Neural　Network)-LSTM　(Long　Short-Term　Memory)　model,　operating　on　the　raw　EEG　in　three　phases:　1)　Morphological　Feature　Extraction,　a　dual-branch　CNN　utilizes　convolution　kernels　of　two　different　scales　to　learn　morphological　features　(individual　sample);　2)　Feature　Reinforcement,　the　dual-scale　features　are　then　reinforced　with　temporal　dependencies　(inter-sample)　captured　by　LSTM;　and　3)　EEG　Reconstruction,　the　resulting　feature　vectors　are　finally　aggregated　to　reconstruct　the　artifact-free　EEG　via　a　terminal　fully　connected　layer.　Extensive　experiments　have　been　performed　to　compare　DuoCL　to　six　state-of-the　-art　counterparts　(e.g.,　1D-ResCNN　and　NovelCNN).　DuoCL　can　reconstruct　more　accurate　waveforms　and　achieve　the　highest　SNR　&　correlation　(CC)　as　well　as　the　lowest　error　(RRMSEt　&　RRMSEf).　In　particular,　DuoCL　holds　potentials　in　providing　a　high-quality　removal　of　unknown　and　hybrid　artifacts.