Embedding Decomposition for Artifacts Removal in EEG Signals

TL;DR

DeepSeparator is a deep learning framework that effectively separates neural signals from artifacts in EEG data, improving denoising performance and interpretability over traditional methods.

Contribution

It introduces a novel embedding decomposition approach with an encoder, decomposer, and decoder for EEG artifact removal, enhancing interpretability and performance.

Findings

Outperforms conventional models in artifact removal tasks

Effective on both semi-synthetic and real EEG datasets

Extensible to multi-channel and variable-length EEG data

Abstract

Electroencephalogram (EEG) recordings are often contaminated with artifacts. Various methods have been developed to eliminate or weaken the influence of artifacts. However, most of them rely on prior experience for analysis. Here, we propose an deep learning framework to separate neural signal and artifacts in the embedding space and reconstruct the denoised signal, which is called DeepSeparator. DeepSeparator employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal. Besides, DeepSeparator can extract the artifact, which largely increases the model interpretability. The proposed method is tested with a semi-synthetic EEG dataset and a real task-related EEG dataset, suggesting that DeepSeparator outperforms the conventional models in both EOG and EMG artifact removal. DeepSeparator can be extended to multi-channel EEG and data of any length. It may motivate future developments and application of deep learning-based EEG denoising. The code for DeepSeparator is available at https://github.com/ncclabsustech/DeepSeparator.