Enhancing Artifact Removal From Scalp EEG Using State-Wise Deep Convolutional Network

Pre-processing is a fundamental step for any tasks reliant on scalp EEG data. The presence of various artifacts in acquired EEG data, which mask the expected features of brain activity, underscores the pivotal role of artifact removal in pre-processing. Recently, deep learning methods have demonstrated superior efficacy in artifact removal compared to conventional methods such as regression, classical filtering, and signal decomposition. While EEG signals, characterized as time series, differ from extensively explored modalities like photos or videos in multimodal machine learning, a suitable network architecture is necessary for the utilization of deep learning methods in EEG artifact removal tasks. Therefore, in this study, we propose a neural network architecture utilizing self-learned state distinction criteria for time series segmentation and tested its artifact removal performance on semi-simulated and real EEG datasets. Notably, our proposed network architecture outperforms the state-of-the-art network in artifact removal performance. Furthermore, the proposed model demonstrates acceptable real-time processing capabilities, thus highlighting its potential applications in real clinical and research settings as an online pre-processing step.