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.

翻译：脑电图（EEG）信号在临床医学、脑科学研究和神经系统疾病研究中具有关键作用。然而，记录到的EEG数据易受多种生理和环境伪迹的干扰，阻碍了对潜在大脑活动的准确分析。去噪技术对于缓解这一挑战至关重要。与传统方法相比，基于深度学习的近期方法在提升EEG数据信噪比方面展现出巨大潜力。在大规模语言模型（LLMs）领域，部分模型常用的Retentive Network（Retnet）基础设施表现出强大的特征提取和全局建模能力。鉴于EEG信号与自然语言之间存在时间相似性，我们将自然语言处理中的Retnet引入EEG去噪。这一融合为EEG去噪提供了新方法，为深入理解大脑活动和神经系统疾病的精确诊断开辟了新途径。然而，由于EEG信号具有一维特性，而自然语言处理处理的是二维数据，直接将Retnet应用于EEG去噪并不可行。为使Retnet适用于EEG去噪，我们提出了信号嵌入方法，将一维EEG信号转换为二维作为网络输入。实验结果验证了所提方法在去噪效果上的显著提升。