  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.
