EEG Reconstruction With a Dual-Scale CNN-LSTM Model for Deep Artifact Removal

Artifact removal has been an open critical issue for decades in tasks centering on EEG analysis. Recent 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 ${\mathsf{SNR}}$ & correlation (${\mathsf{CC}}$) as well as the lowest error (${\mathsf{RRMSE}}_{\mathsf{t}}$ & ${\mathsf{RRMSE}}_{\mathsf{f}}$). In particular, DuoCL holds potentials in providing a high-quality removal of unknown and hybrid artifacts.