Scholarlink

<p>Recent technological advancements have led to a significant increase in electroencephalogram (EEG)-based applications, ranging from clinical diagnosis and brain computer interfaces (BCI) to sleep studies and the monitoring of cognitive tasks. However, raw EEG signals are highly susceptible to artifacts, which hinder accurate analysis and interpretation of brain signals. Traditional artifact removal techniques often fall short owing to their linear assumptions and limited generalizability. Deep learning (DL) based approaches have shown remarkable potential for capturing nonlinear and complex features of EEG signals. This review provides a comprehensive overview of state-of-the-art deep learning models developed for EEG denoising, highlighting their architectural designs, strengths, limitations, and performance analysis. In addition, the study emphasizes the importance of accurate evaluation frameworks and benchmarking, as well as the challenges of generalizability and interpretability. Future research directions include the integration of hybrid architectures, self-supervised learning, and real-time implementation. This article serves as a resource for researchers aiming to advance EEG denoising through modern deep learning approaches.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A comprehensive review of deep learning models for denoising EEG signals: challenges, advances, and future directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月22日

查看全文
  • Vandana Akshath Raj,
  • Tejasvi Parupudi,
  • Ananthakrishna Thalengala,
  • Subramanya G. Nayak

摘要

Recent technological advancements have led to a significant increase in electroencephalogram (EEG)-based applications, ranging from clinical diagnosis and brain computer interfaces (BCI) to sleep studies and the monitoring of cognitive tasks. However, raw EEG signals are highly susceptible to artifacts, which hinder accurate analysis and interpretation of brain signals. Traditional artifact removal techniques often fall short owing to their linear assumptions and limited generalizability. Deep learning (DL) based approaches have shown remarkable potential for capturing nonlinear and complex features of EEG signals. This review provides a comprehensive overview of state-of-the-art deep learning models developed for EEG denoising, highlighting their architectural designs, strengths, limitations, and performance analysis. In addition, the study emphasizes the importance of accurate evaluation frameworks and benchmarking, as well as the challenges of generalizability and interpretability. Future research directions include the integration of hybrid architectures, self-supervised learning, and real-time implementation. This article serves as a resource for researchers aiming to advance EEG denoising through modern deep learning approaches.