1. |
胡志安, 何超. 2019年中国脑科学研究进展. 第三军医大学学报, 2020, 42(5): 431-436.
|
2. |
王冰冰, 许泽举, 罗通, 等. 基于脑电信号的青少年注意力检测和训练系统. 计算机系统应用, 2021, 30(10): 76-85.
|
3. |
Chiang H S, Hsiao K L, Liu L C. EEG-based detection model for evaluating and improving learning attention. Journal of Medical and Biological Engineering, 2018, 38: 847-856.
|
4. |
Krüger V, Mahlmeister U, Sommer G. Attentive face detection and recognition// Mustererkennung 1998, Berlin: Springer Berlin Heidelberg, 1998: 279-286.
|
5. |
Kuo Y L, Lee J S, Hsieh M C. Video-based eye tracking to detect the attention shift: A computer classroom context-aware system. International Journal of Distance Education Technologies (IJDET), 2014, 12(4): 66-81.
|
6. |
Ni D, Wang S, Liu G. The EEG-based attention analysis in multimedia m-learning. Computational and Mathematical Methods in Medicine, 2020, 2020: 4837291.
|
7. |
Al-Nafjan A, Aldayel M. Predict students’ attention in online learning using EEG data. Sustainability, 2022, 14(11): 6553.
|
8. |
Atilla F, Alimardani M. EEG-based classification of drivers attention using convolutional neural network//2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS), Magdeburg: IEEE, 2021.
|
9. |
Miranda P, Cox C D, Alexander M, et al. In quest of pathognomonic/endophenotypic markers of attention deficit hyperactivity disorder (ADHD): potential of EEG-based frequency analysis and ERPs to better detect, prevent and manage ADHD. Medical Devices: Evidence and Research, 2020, 13: 115-137.
|
10. |
Kang S H, Park J H, Shin S W, et al. Analysis of EEG signals for attention training game contents. The Journal of The Institute of Internet, Broadcasting and Communication, 2019, 19(3): 83-90.
|
11. |
Xie Y, Oniga S. A review of processing methods and classification algorithm for EEG signal. Carpathian Journal of Electronic and Computer Engineering, 2020, 13(1): 23-29.
|
12. |
胡理, 张治国. 脑电信号处理与特征提取. 北京: 科学出版社, 2020: 79-80.
|
13. |
Bisht A, Kaur C, Singh P. Recent advances in artifact removal techniques for EEG signal processing. Intelligent Communication, Control and Devices: Proceedings of ICICCD 2018, 2020, 989: 385-392.
|
14. |
欧阳天雄. 基于脑电信号的情感识别方法研究. 北京: 北京邮电大学, 2021.
|
15. |
Pincus S M. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U S A, 1991, 88(6): 2297-2301.
|
16. |
Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000, 278(6): 2039-2049.
|
17. |
印想. 基于安卓平台的注意力检测系统的研究. 武汉: 中南民族大学, 2019.
|
18. |
徐鲁强, 刘静霞, 肖光灿, 等. 脑电注意水平的特征识别. 计算机应用, 2012, 32(11): 3268-3270.
|
19. |
Alirezaei M, Sardouie S H. Detection of human attention using EEG signals//2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME), Tehran: IEEE, 2017: 1-5.
|
20. |
van Son D, De Blasio F M, Fogarty J S, et al. Frontal EEG theta/beta ratio during mind wandering episodes. Biological psychology, 2019, 140: 19-27.
|
21. |
Liu N H, Chiang C Y, Chu H C. Recognizing the degree of human attention using EEG signals from mobile sensors. Sensors, 2013, 13(8): 10273-10286.
|
22. |
Bai L, Guo J, Xu T, et al. Emotional monitoring of learners based on EEG signal recognition. Procedia Computer Science, 2020, 174(1): 364-368.
|
23. |
Clarke A R, Barry R J, Johnstone S. Resting state EEG power research in attention-deficit/hyperactivity disorder: a review update. Clinical Neurophysiology, 2020, 131(7): 1463-1479.
|
24. |
汤永超. 基于脑机接口的注意力分析方法的研究. 广州: 华南理工大学, 2021.
|
25. |
吴欢, 印想, 官金安. 频带能量与样本熵在注意力脑电信号中的对比研究. 计算机与数字工程, 2020, 48(03): 603-606, 622.
|
26. |
杨埔. 注意成分的相互关系及其功能脑网络研究. 成都: 电子科技大学, 2019.
|
27. |
刘素杰. 基于脑网络测度的注意力脑电分级研究. 郑州: 郑州大学, 2018.
|
28. |
Rahman M A, Hossain M F, Hossain M, et al. Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal. Egyptian Informatics Journal, 2020, 21(1): 23-35.
|
29. |
Mercado-Aguirre I M, Gutierrez-Ruiz K P, Contreras-Ortiz S H. EEG feature selection for ADHD detection in children//16th International Symposium on Medical Information Processing and Analysis, Basel: SPIE, 2020(11583): 238-246.
|
30. |
栗然, 丁星, 孙帆, 等. 基于Wide & Deep-XGB2LSTM模型的超短期光伏功率预测. 电力自动化设备, 2021, 41(7): 31-37.
|
31. |
李小伟. 脑电、眼动信息与学习注意力及抑郁的中文相关性研究. 兰州: 兰州大学, 2015.
|
32. |
Chauhan V K, Dahiya K, Sharma A. Problem formulations and solvers in linear SVM: a review. Artificial Intelligence Review, 2019, 52(2): 803-855.
|
33. |
刘家俊. 基于单通道脑电的注意力训练系统研究. 郑州: 郑州大学, 2021.
|
34. |
Li Y, Li X, Ratcliffe M, et al. A real-time EEG-based BCI system for attention recognition in ubiquitous environment//2011 International Workshop on Ubiquitous Affective Awareness and Intelligent Interaction, Beijing: Association for Computing Machinery, 2011: 33-40.
|
35. |
Ghasemy H, Momtazpour M, Sardouie S H. Detection of sustained auditory attention in students with visual impairment//2019 27th Iranian Conference on Electrical Engineering (ICEE), Yazd: IEEE, 2019: 1798-1801.
|
36. |
Vallabhaneni R B, Sharma P, Kumar V, et al. Deep learning algorithms in EEG signal decoding application: a review. IEEE Access, 2021, 9: 125778- 125786.
|
37. |
Alhalaseh R, Alhalaseh S. Machine-learning-based emotion recognition system using EEG signals. Computers, 2020, 9(4): 95.
|
38. |
Wang Y, Shi Y, Du J, et al. A CNN-based personalized system for attention detection in wayfinding tasks. Advanced Engineering Informatics, 2020, 46: 101180.
|
39. |
Hassan R, Hasan S, Hasan M J, et al. Human attention recognition with machine learning from brain-EEG signals//2020 IEEE 2nd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), Tainan: IEEE, 2020: 16-19.
|
40. |
Toa C K, Sim K S, Tan S C. Electroencephalogram-based attention level classification using convolution attention memory neural network. IEEE Access, 2021, 9: 58870-58881.
|
41. |
Ko L W, Komarov O, Lai W k, et al. Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention task. Journal of Neural Engineering, 2020, 17(3): 036015.
|
42. |
Chen J, Ro T, Zhu Z. Emotion recognition with audio, video, EEG, and EMG: a dataset and baseline approaches. IEEE Access, 2022, 10: 13229-13242.
|