Attention Detection Using EEG Signals and Machine Learning: A Review  

作　　者：Qianru Sun Yueying Zhou Peiliang Gong Daoqiang Zhang 

机构地区：[1]Key Laboratory of Brain-Machine Intelligence Technology,Ministry of Education,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China [2]College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China

出　　处：《Machine Intelligence Research》2025年第2期219-238,共20页机器智能研究(英文版)

基　　金：supported by the National Natural Science Foundation of China(Nos.62136004,62276130 and 62406131);the National Key R&D Program of China(No.2023YFF1204803);Key Research and Development Plan of Jiangsu Province,China(No.BE2022842).

摘　　要：Attention detection using electroencephalogram(EEG)signals has become a popular topic.However,there seems to be a notable gap in the literature regarding comprehensive and systematic reviews of machine learning methods for attention detection using EEG signals.Therefore,this survey outlines recent advances in EEG-based attention detection within the past five years,with a primary focus on auditory attention detection(AAD)and attention level classification.First,we provide a brief overview of commonly used paradigms,preprocessing techniques,and artifact-handling methods,as well as listing accessible datasets used in these studies.Next,we summarize the machine learning methods for classification in this field and divide them into two categories:traditional machine learning methods and deep learning methods.We also analyse the most frequently used methods and discuss the factors influencing each technique′s performance and applicability.Finally,we discuss the existing challenges and future trends in this field.

关 键 词：Attention detection electroencephalogram(EEG) machine learning deep learning brain-computer interface. 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP391[自动化与计算机技术—计算机科学与技术]