EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive Network

TL;DR

This paper introduces EEGDiR, a novel EEG denoising network inspired by natural language processing's Retentive Network, employing a signal embedding method to improve noise reduction in EEG signals.

Contribution

The paper adapts the Retentive Network for EEG denoising by developing a signal embedding technique to handle one-dimensional EEG data, enhancing denoising performance.

Findings

Significant improvement in EEG denoising effectiveness.

Effective adaptation of Retentive Network for 1D EEG signals.

Potential for better brain activity analysis and neurological diagnosis.

Abstract

Electroencephalogram (EEG) signals play a pivotal role in clinical medicine, brain research, and neurological disease studies. However, susceptibility to various physiological and environmental artifacts introduces noise in recorded EEG data, impeding accurate analysis of underlying brain activity. Denoising techniques are crucial to mitigate this challenge. Recent advancements in deep learningbased approaches exhibit substantial potential for enhancing the signal-to-noise ratio of EEG data compared to traditional methods. In the realm of large-scale language models (LLMs), the Retentive Network (Retnet) infrastructure, prevalent for some models, demonstrates robust feature extraction and global modeling capabilities. Recognizing the temporal similarities between EEG signals and natural language, we introduce the Retnet from natural language processing to EEG denoising. This integration presents a novel approach to EEG denoising, opening avenues for a profound understanding of brain activities and accurate diagnosis of neurological diseases. Nonetheless, direct application of Retnet to EEG denoising is unfeasible due to the one-dimensional nature of EEG signals, while natural language processing deals with two-dimensional data. To facilitate Retnet application to EEG denoising, we propose the signal embedding method, transforming one-dimensional EEG signals into two dimensions for use as network inputs. Experimental results validate the substantial improvement in denoising effectiveness achieved by the proposed method.