Cross-task-oriented EEG signal analysis methods: Our opinion.
作者信息
Wen Dong, Pang Zhenhua, Wan Xianglong, Li Jingjing, Dong Xianling, Zhou Yanhong
机构信息
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China.
Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems, Ministry of Education, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China.
出版信息
Front Neurosci. 2023 Mar 9;17:1153060. doi: 10.3389/fnins.2023.1153060. eCollection 2023.
Abstract
摘要
相似文献
1
Cross-task-oriented EEG signal analysis methods: Our opinion.面向跨任务的脑电图信号分析方法：我们的观点。
Front Neurosci. 2023 Mar 9;17:1153060. doi: 10.3389/fnins.2023.1153060. eCollection 2023.
2
Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification.用于基于脑电图信号的独立于个体的想象心理任务分类的深度神经网络
Diagnostics (Basel). 2023 Feb 9;13(4):640. doi: 10.3390/diagnostics13040640.
3
[Extraction of the EEG signal feature based on echo state networks].基于回声状态网络的脑电信号特征提取
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2012 Apr;29(2):206-11.
4
A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.基于主成分分析的脑电信号判别特征提取的互协方差方法。
Comput Methods Programs Biomed. 2017 Jul;146:47-57. doi: 10.1016/j.cmpb.2017.05.009. Epub 2017 May 24.
5
Fuzzy clustering-based feature extraction method for mental task classification.基于模糊聚类的心理任务分类特征提取方法
Brain Inform. 2017 Jun;4(2):135-145. doi: 10.1007/s40708-016-0056-0. Epub 2016 Sep 3.
6
Epileptic seizure detection in EEG signal with GModPCA and support vector machine.基于广义模态主成分分析（GModPCA）和支持向量机的脑电图（EEG）信号癫痫发作检测
Biomed Mater Eng. 2017;28(2):141-157. doi: 10.3233/BME-171663.
7
Trends in EEG signal feature extraction applications.脑电图信号特征提取应用的趋势。
Front Artif Intell. 2023 Jan 25;5:1072801. doi: 10.3389/frai.2022.1072801. eCollection 2022.
8
EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres.基于跨频率格兰杰因果关系特征提取与左右半球融合的脑电图情感识别
Front Neurosci. 2022 Sep 7;16:974673. doi: 10.3389/fnins.2022.974673. eCollection 2022.
9
EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals.EPSPatNet86：一种基于 EEG 信号的 ADHD 障碍检测的八叉星模式学习网络。
Physiol Meas. 2022 Apr 4;43(3). doi: 10.1088/1361-6579/ac59dc.
10
Epileptic seizure detection in EEG signal using machine learning techniques.使用机器学习技术检测脑电图（EEG）信号中的癫痫发作
Australas Phys Eng Sci Med. 2018 Mar;41(1):81-94. doi: 10.1007/s13246-017-0610-y. Epub 2017 Dec 20.
引用本文的文献
1
Integrating music therapy and video games in cognitive interventions: innovative applications of closed-loop EEG.将音乐疗法和电子游戏整合到认知干预中：闭环脑电图的创新应用。
Front Aging Neurosci. 2024 Dec 18;16:1498821. doi: 10.3389/fnagi.2024.1498821. eCollection 2024.
本文引用的文献
1
EEG Based Dynamic Functional Connectivity Analysis in Mental Workload Tasks With Different Types of Information.基于脑电图的不同类型信息下脑力工作负荷任务的动态功能连接分析。
IEEE Trans Neural Syst Rehabil Eng. 2022;30:632-642. doi: 10.1109/TNSRE.2022.3156546. Epub 2022 Mar 21.
2
Cross-Task Cognitive Workload Recognition Based on EEG and Domain Adaptation.基于 EEG 和领域自适应的跨任务认知工作负荷识别。
IEEE Trans Neural Syst Rehabil Eng. 2022;30:50-60. doi: 10.1109/TNSRE.2022.3140456. Epub 2022 Jan 28.
3
Cross-Task Consistency of Electroencephalography-Based Mental Workload Indicators: Comparisons Between Power Spectral Density and Task-Irrelevant Auditory Event-Related Potentials.基于脑电图的心理负荷指标的跨任务一致性：功率谱密度与任务无关听觉事件相关电位的比较
Front Neurosci. 2021 Nov 16;15:703139. doi: 10.3389/fnins.2021.703139. eCollection 2021.
4
Label-Based Alignment Multi-Source Domain Adaptation for Cross-Subject EEG Fatigue Mental State Evaluation.基于标签对齐的多源域自适应用于跨个体脑电图疲劳精神状态评估
Front Hum Neurosci. 2021 Oct 1;15:706270. doi: 10.3389/fnhum.2021.706270. eCollection 2021.
5
Generalized Deep Learning EEG Models for Cross-Participant and Cross-Task Detection of the Vigilance Decrement in Sustained Attention Tasks.基于广义深度学习的脑电模型在持续性注意任务警觉下降的跨被试和跨任务检测。
Sensors (Basel). 2021 Aug 20;21(16):5617. doi: 10.3390/s21165617.
6
A deep descriptor for cross-tasking EEG-based recognition.一种用于基于脑电图的跨任务识别的深度描述符。
PeerJ Comput Sci. 2021 May 19;7:e549. doi: 10.7717/peerj-cs.549. eCollection 2021.
7
EEG Fingerprints of Task-Independent Mental Workload Discrimination.基于脑电图的任务无关脑力负荷识别特征。
IEEE J Biomed Health Inform. 2021 Oct;25(10):3824-3833. doi: 10.1109/JBHI.2021.3085131. Epub 2021 Oct 5.
8
Continuous decoding of cognitive load from electroencephalography reveals task-general and task-specific correlates.从脑电图中持续解码认知负荷揭示了任务通用和任务特定的相关性。
J Neural Eng. 2020 Oct 16;17(5):056016. doi: 10.1088/1741-2552/abb9bc.
9
Learning Spatial-Spectral-Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment.基于循环三维卷积神经网络学习空间-谱-时 EEG 特征进行跨任务精神工作负荷评估。
IEEE Trans Neural Syst Rehabil Eng. 2019 Jan;27(1):31-42. doi: 10.1109/TNSRE.2018.2884641. Epub 2018 Dec 3.
10
An Evaluation of EEG-based Metrics for Engagement Assessment of Distance Learners.基于脑电图的远程学习者参与度评估指标研究
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:307-310. doi: 10.1109/EMBC.2018.8512302.
Zotero 插件