Research progress on attention level evaluation based on electroencephalogram signals_Journal of Biomedical Engineering

Authors：

 YANG Wenyang , ZHANG Wenxuan

College of Computer, Xi'an Shiyou University, Xi’an 710065, P. R. China;

Corresponding?author：

Keywords：

Attention level evaluation; Electroencephalogram signals; Feature extraction; Feature selection

DOI：

10.7507/1001-5515.202208085

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Attention level evaluation refers to the evaluation of people's attention level through observation or experimental testing, and its research results have great application value in education and teaching, intelligent driving, medical health and other fields. With its objective reliability and security, electroencephalogram signals have become one of the most important technical means to analyze and express attention level. At present, there is little review literature that comprehensively summarize the application of electroencephalogram signals in the field of attention evaluation. To this end, this paper first summarizes the research progress on attention evaluation; then the important methods for electroencephalogram attention evaluation are analyzed, including data preprocessing, feature extraction and selection, attention evaluation methods, etc.; finally, the shortcomings of the current development in the field of electroencephalogram attention evaluation are discussed, and the future development trend is prospected, to provide research references for researchers in related fields.

Citation： YANG Wenyang, ZHANG Wenxuan. Research progress on attention level evaluation based on electroencephalogram signals. Journal of Biomedical Engineering, 2023, 40(4): 820-828. doi: 10.7507/1001-5515.202208085 Copy

1.	胡志安, 何超. 2019年中國腦科學研究進展. 第三軍醫大學學報, 2020, 42(5): 431-436.
2.	王冰冰, 許澤舉, 羅通, 等. 基于腦電信號的青少年注意力檢測和訓練系統. 計算機系統應用, 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, 張治國. 腦電信號處理與特征提取. 北京: 科學出版社, 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.	歐陽天雄. 基于腦電信號的情感識別方法研究. 北京: 北京郵電大學, 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. 基于安卓平臺的注意力檢測系統的研究. 武漢: 中南民族大學, 2019.
18.	徐魯強, 劉靜霞, 肖光燦, 等. 腦電注意水平的特征識別. 計算機應用, 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.	湯永超. 基于腦機接口的注意力分析方法的研究. 廣州: 華南理工大學, 2021.
25.	吳歡, 印想, 官金安. 頻帶能量與樣本熵在注意力腦電信號中的對比研究. 計算機與數字工程, 2020, 48(03): 603-606, 622.
26.	楊埔. 注意成分的相互關系及其功能腦網絡研究. 成都: 電子科技大學, 2019.
27.	劉素杰. 基于腦網絡測度的注意力腦電分級研究. 鄭州: 鄭州大學, 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, 孫帆, 等. 基于Wide & Deep-XGB2LSTM模型的超短期光伏功率預測. 電力自動化設備, 2021, 41(7): 31-37.
31.	李小偉. 腦電、眼動信息與學習注意力及抑郁的中文相關性研究. 蘭州: 蘭州大學, 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.	劉家俊. 基于單通道腦電的注意力訓練系統研究. 鄭州: 鄭州大學, 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.

1. 胡志安, 何超. 2019年中國腦科學研究進展. 第三軍醫大學學報, 2020, 42(5): 431-436.
2. 王冰冰, 許澤舉, 羅通, 等. 基于腦電信號的青少年注意力檢測和訓練系統. 計算機系統應用, 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, 張治國. 腦電信號處理與特征提取. 北京: 科學出版社, 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. 歐陽天雄. 基于腦電信號的情感識別方法研究. 北京: 北京郵電大學, 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. 基于安卓平臺的注意力檢測系統的研究. 武漢: 中南民族大學, 2019.
18. 徐魯強, 劉靜霞, 肖光燦, 等. 腦電注意水平的特征識別. 計算機應用, 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. 湯永超. 基于腦機接口的注意力分析方法的研究. 廣州: 華南理工大學, 2021.
25. 吳歡, 印想, 官金安. 頻帶能量與樣本熵在注意力腦電信號中的對比研究. 計算機與數字工程, 2020, 48(03): 603-606, 622.
26. 楊埔. 注意成分的相互關系及其功能腦網絡研究. 成都: 電子科技大學, 2019.
27. 劉素杰. 基于腦網絡測度的注意力腦電分級研究. 鄭州: 鄭州大學, 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, 孫帆, 等. 基于Wide & Deep-XGB2LSTM模型的超短期光伏功率預測. 電力自動化設備, 2021, 41(7): 31-37.
31. 李小偉. 腦電、眼動信息與學習注意力及抑郁的中文相關性研究. 蘭州: 蘭州大學, 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. 劉家俊. 基于單通道腦電的注意力訓練系統研究. 鄭州: 鄭州大學, 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.

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