Key result
A proposed segmentation-denoising network (SDNet) outperformed state-of-the-art approaches for artifact removal from single-channel EEG data by differentiating noisy segments from clean ones.
Why the study?
Existing deep learning-based EEG denoising methods often distort clean segments by assuming noise spans the entire recording rather than differentiating clean from noisy segments.
SDNet provides a novel, effective method for removing artifacts from single-channel EEG by differentiating noisy from clean segments.
Supports development of improved EEG denoising tools; leaves open clinical validation in cardiovascular monitoring.
As an important neurorecording technique, electroencephalography (EEG) is often contaminated by various artifacts, which obstructs subsequent analysis. In recent years, deep learning-based (DL-based) methods have been proven to be promising for artifact removal. However, most denoising methods focus on recovering clean EEG from raw signals contaminated by the noise over the entire recording period, ignoring that the practical EEG recordings may contain clean segments in addition to noise segments. Therefore, the general model may cause distortion when dealing with clean segments. In this article, we propose a simple, yet effective segmentation-denoising network (SDNet) for artifact removal. The proposed method is capable of differentiating noisy EEG segments from clean ones via semantic segmentation, avoiding the distortion caused by processing clean segments. We conduct a performance comparison on semisimulated and real EEG data. The experimental results demonstrate that SDNet outperforms the state-of-art approaches. This work provides a novel way to reconstruct artifact-attenuated EEG signals, and may further benefit the EEG-based diagnosis and treatment.
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Li et al. (2023) studied EEG artifacts. Segmentation-denoising network (SDNet) vs. State-of-the-art approaches was evaluated on Artifact removal performance. A proposed segmentation-denoising network (SDNet) outperformed state-of-the-art approaches for artifact removal from single-channel EEG data by differentiating noisy segments from clean ones.
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