CT-DCENet: Deep EEG Denoising via CNN-Transformer-Based Dual-Stage Collaborative Ensemble Learning.

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.