基于深度学习的EEG数据分析技术综述  

Survey of deep learning based EEG data analysis technology

作　　者：钟博 王鹏飞[1] 王乙乔 王晓玲[1] ZHONG Bo;WANG Pengfei;WANG Yiqiao;WANG Xiaoling(School of Computer Science and Technology,East China Normal University,Shanghai 200062,China)

机构地区：[1]华东师范大学计算机科学与技术学院,上海200062

出　　处：《浙江大学学报（工学版）》2024年第5期879-890,共12页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(61972155)。

摘　　要：对近年来的相关工作进行全面分析、横向比较,梳理出基于深度学习的EEG数据分析闭环流程.对EEG数据进行介绍,从深度学习在EEG数据预处理、特征提取以及模型泛化3个关键阶段的应用进行展开,梳理深度学习算法在相应阶段提供的研究思路和解决方案,包括各阶段所存在的难点与问题.全方位总结出不同算法的主要贡献和局限性,讨论深度学习技术在各个阶段处理EEG数据时所面临的挑战及未来的发展方向.A thorough analysis and cross-comparison of recent relevant works was provided,outlining a closed-loop process for EEG data analysis based on deep learning.EEG data were introduced,and the application of deep learning in three key stages:preprocessing,feature extraction,and model generalization was unfolded.The research ideas and solutions provided by deep learning algorithms in the respective stages were delineated,including the challenges and issues encountered at each stage.The main contributions and limitations of different algorithms were comprehensively summarized.The challenges faced and future directions of deep learning technology in handling EEG data at each stage were discussed.

关 键 词：头皮脑电(EEG) 闭环流程 深度学习 预处理 特征提取 模型泛化 

分 类 号：TP392[自动化与计算机技术—计算机应用技术]