Essentia: Boosting Artifact Removal from EEG through Semantic Guidance Utilizing Diffusion Model

Abstract

Electroencephalography (EEG) is a time-series signal containing semantic information that can be used to determine human brain activities. Artifacts within EEG data can interfere with the intrinsic distribution of this semantic information, so removing artifacts is crucial for improving EEG analysis performance on downstream tasks. In this paper, we redefine the efficacy of the artifact removal model by evaluating the performance of the noisy EEG data in downstream tasks before and after artifact removal. Currently, most artifact removal models fail to ensure semantic consistency, rendering them ineffective. To solve it, we propose an artifact removal model based on the 1-dimensional diffusion model utilizing the U-Net, referred to as Essentia. Moreover, we find that the skip-connection layer in U-Net contains mid-to-high-frequency information that interferes with the semantic representation. We introduce a semantic guidance module (SGM) that leverages contrastive learning to generate semantic distribution weights, boosting semantic representation. We evaluate Essentia on three datasets with six solutions. The accuracy of downstream tasks from the denoised EEG data increased by 4% compared with the DeepSeparetor. The code and models are available at https://github.com/NKU-EmbeddedSystem/Essentia.