基于脑电功率谱密度的作业人员脑力负荷评估方法

doi:10.7527/S1000-6893.2019.23618

Abstract

Abstract: The accurate recognition of mental workload levels is of great significance to reduce human accidents caused by operators with invalid mental workload. This paper focuses on the objective mental workload assessment of operators in human-machine system. An aviation situational experiment based on MATB-Ⅱ was carried out at three levels of mental workload. Sixteen subjects were asked to fill in the NASA-Task Load Index (NASA-TLX) scale and the Electroencephalogram (EEG) results during the experiment were recorded. By analyzing the collected subjective and physiological data, a subject-specified mental workload assessment method was proposed using the Power Spectral Density (PSD) of EEG and the Support Vector Machine (SVM). The results show that the subjective mental workload scores increase significantly (p<0.001) with the increase of designed mental workload levels, indicating that the experimental design successfully induces different mental workload scenarios. Based on the rationality of the experimental design, the subject-specified mental workload assessment models are established, and the parameters of these models are optimized by grid search and then unified as the penalty parameter of 3 000 and the kernel function parameter of 0.000 1. The test accuracy reaches 0.966 5±0.029 8, and the area under Macro-Averaging receiver operating Characteristic curve (Macro-AUC) reaches 0.991 0±0.011 4. Thus, the models provide a new approach for the objective and accurate assessment of mental workload, providing a basis for the real-time discrimination of mental workload.

Key words: mental workload, NASA-task load index, power spectral density, support vector machine, subject-specified discrimination model

CLC Number:

V7
R857.11

ZHANG Jie, PANG Liping, WANYAN Xiaoru, CHEN Hao, WANG Xin, LIANG Jin. Method for operator mental workload assessment based on power spectral density of EEG[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(10): 123618-123618.

References

[1] WILSON G F, CALDWELL J A, WESENSTEN N J. Operator functional state assessment for adaptive automation implementation[J].Proceedings of SPIE-The International Society for Optical Engineering, 2005, 5797:100-104.
[2] 完颜笑如, 庄达民. 飞行员脑力负荷测量与应用[M]. 北京:科学出版社, 2014:15, 49-52. WANYAN X R, ZHUANG D M. Measurement and application of mental workload in pilots[M]. Beijing:Science Press, 2014:15, 49-52(in Chinese).
[3] RADUNTZ T. Dual frequency head maps:A new method for indexing mental workload continuously during execution of cognitive tasks[J].Frontiers in Physiology, 2017, 8:1019.
[4] 郭孜政, 潘雨帆, 潘毅润, 等. 驾驶员脑力负荷的SVM识别模型[J].哈尔滨工业大学学报, 2016, 48(3):154-158. GUO Z Z, PAN Y F, PAN Y R, et al. SVM recognition model of driver's mental workload[J].Journal of Harbin Institute of Technology, 2016, 48(3):154-158(in Chinese).
[5] AYAZ H, SHEWOKIS P A, BUNCE S, et al. Optical brain monitoring for operator training and mental workload assessment[J].NeuroImage, 2012, 59(1):36-47.
[6] WANYAN X R, ZHUANG D M, LIN Y, et al. Influence of mental workload on detecting information varieties revealed by mismatch negativity during flight simulation[J].International Journal of Industrial Ergonomics, 2018, 64:1-7.
[7] RYU K, MYUNG R, ERGON J. Evaluation of mental workload with a combined measure based on physiological indices during a dual task of tracking and mental arithmetic[J].International Journal of Industrial Ergonomics, 2005, 35(11):991-1009.
[8] 白杰, 冯传宴, 杨坤. 飞行员脑力负荷生理测量方法研究进展[J].航天医学与医学工程, 2016, 29(2):150-156. BAI J, FENG C Y, YANG K. Research progress of physiological measurement of mental workload in pilots[J].Space Medicine & Medical Engineering, 2016, 29(2):150-156(in Chinese).
[9] 明东, 柯余峰, 何峰, 等. 基于生理信号的脑力负荷检测及自适应自动化系统研究:40年回顾与最新进展[J].电子测量与仪器学报, 2015, 29(1):1-13. MING D, KE Y F, HE F, et al. Psychophysiological measurement based studies on mental workload assessment and adaptive automation:Review of the last 40 years and the latest developments[J].Journal of Electronic Measurement and Instrumentation, 2015, 29(1):1-13(in Chinese).
[10] STEVEN L, BENJAMIN B, THORSTEN D, et al. Introduction to machine learning for brain imaging[J].NeuroImage, 2011, 56(2):387-399.
[11] GARRETT L D, ANDERSON B W, THAUT M H, et al. Comparison of linear, nonlinear, and feature selection methods for EEG signal classification[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 11(2):141-144.
[12] SUN Z, QIAO Y, LELIEVELDT B P F, et al. Integrating spatial-anatomical regularization and structure sparsity into SVM:Improving interpretation of Alzheimer's disease classification[J].NeuroImage, 2018, 178:445-460.
[13] SWIDERSKI B, OSOWSKI S, CICHOCKI A, et al. Single-class SVM and directed transfer function approach to the localization of the region containing epileptic focus[J].Neurocomputing, 2009, 72(7-9):1575-1583.
[14] BARJINDER K, DINESH S, PARTHA P R. EEG based emotion classification mechanism in BCI[J].Procedia Computer Science, 2018, 132:752-758.
[15] MIRANDA P B C, PRUDENCIO R B C, CARVALHO A P L F D, et al. A hybrid meta-learning architecture for multi-objective optimization of SVM parameters[J].Neurocomputing, 2014, 143:27-43.
[16] 冯传宴, 完颜笑如, 刘双, 等. 负荷条件下注意力分配策略对情境意识的影响[J].航空学报, 2019, 41(3):123307. FENG C Y, WANYAN X R, LIU S, et al. Influence of different attention allocation strategies under workloads on situation awareness[J].Acta Aeronautica et Astronautica Sinica, 2020, 41(3):123307(in Chinese).
[17] WILSON G F, RUSSELL C A. Operator functional state classification using multiple psychophysiological features in an air traffic control task[J].Human Factors:The Journal of the Human Factors and Ergonomics Society, 2003, 45(3):381-389.
[18] 赵仑. ERPs实验教程[M]. 南京:东南大学出版社, 2010:137-140. ZHAO L. ERPs experimental tutorial[M]. Nanjing:Southeast University Press, 2010:137-140(in Chinese).
[19] 范晓丽, 牛海燕, 周前祥, 等. 基于EEG的脑力疲劳特征研究[J].北京航空航天大学学报, 2016, 42(7):1406-1413. FAN X L, NIU H Y, ZHOU Q X, et al. Mental fatigue characteristics based on EEG analysis[J].Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(7):1406-1413(in Chinese).
[20] 周志华. 机器学习[M]. 北京:清华大学出版社, 2016:33-35, 121-129. ZHOU Z H. Machine learning[M]. Beijing:Tsinghua University Press, 2016:33-35, 121-129(in Chinese).
[21] BURGES C J C. A tutorial on support vector machines for pattern recognition[J].Data Mining and Knowledge Discovery, 1998, 2(2):121-167.
[22] 奉国和. SVM分类核函数及参数选择比较[J].计算机工程与应用, 2011, 47(3):123-124. FENG G H. Parameter optimizing for support vector machines classification[J].Computer Engineering and Application, 2011, 47(3):123-124(in Chinese).
[23] BRAGA I, DO CARMO L P, BENATTI C C, et al. A note on parameter selection for support vector machines[C]//Mexican International Conference on Artificial Intelligence-Advances in Soft Computing and Its Applications. Berlin:Springer, 2013:233-244.
[24] 李颖洁, 邱意弘, 朱贻盛. 脑电信号分析方法与应用[M]. 北京:科学出版社, 2009:13-14. LI Y J, QIU Y H, ZHU Y S. Analysis method and application of EEG signal[M]. Beijing:Science Press, 2009:13-14(in Chinese).
[25] WILSON G F, RUSSELL C A. Real-time assessment of mental workload using psychophysiological measures and artificial neural networks[J].Human Factors:The Journal of the Human Factors and Ergonomics Society, 2003, 45(4):635-643.
[26] WILSON G F, RUSSELL C A. Operator functional state classification using multiple psychophysiological features in an air traffic control task[J].Human Factors:The Journal of the Human Factors and Ergonomics Society, 2003, 45(3):381-389.
[27] LIU N H, CHIANG C Y, CHU H C. Recognizing the degree of human attention using EEG signals from mobile sensors[J].Sensors, 2013, 13(8):10273-10286.
[28] YOUNGWORTH R N, GALLAGHER B B, STAMPER B L. An overview of power spectral density (PSD) calculations[C]//Proceeding of SPIE-The International Society for Optical Engineering. Bellingham:Society of Photo-optical Instrumentation Engineers, 2005:206-216.

Method for operator mental workload assessment based on power spectral density of EEG

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Tongzhou GAO, Xiaofan HE, Xiaolei WANG, Ziguang LI, Zhentao ZHU, Zhixin ZHAN. Fatigue life prediction of 2014-T6 aluminum alloy based on CDM theory and SVM model [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(7): 228952-228952.
[2]	Jiahui HE, Zhijun CHENG, Bo GUO. Telemetry anomaly detection method based on joint dictionary learning and OCSVM [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(13): 327931-327931.
[3]	CHEN Senlin, GAO Zhenghong, ZHU Xinqi, PANG Chao, DU Yiming, CHEN Shusheng. Unsteady aerodynamic modeling of unstable dynamic process [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(8): 123675-123675.
[4]	HE Qiang, TAN Deqiang, CHENG Lin. Eye movement and fatigue detection in visual inspection of civil aircraft composite materials [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(5): 223532-223532.
[5]	FENG Chuanyan, WANYAN Xiaoru, LIU Shuang, CHEN Hao, ZHUANG Damin, WANG Xin. Influence of different attention allocation strategies under workloads on situation awareness [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(3): 123307-123307.
[6]	TONG Fulin, ZHOU Guiyu, ZHOU Hao, ZHANG Peihong, LI Xinliang. Statistical characteristics of wall shear stress in shock wave and turbulent boundary layer interactions [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(5): 122504-122504.
[7]	YU Mingyue, CHEN Guo, LIU Yongquan, JIANG Guangyi, LI Chenggang, FENG Guoquan, WANG Deyou. Aero-engine Rotor-stator Rubbing Position Identification Based on Casing Strain Signals [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(6): 1474-1484.
[8]	XU Zheping, LANG Rongling, DENG Xiaole. Uncertainty Analysis of Aircraft Flight Parameters Prediction [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, (6): 1100-1107.
[9]	YANG Lin, WANG Congqing, JIANG Longsheng. Signal Recognition of Imbalanced Samples for CVR Based on Fuzzy SVM [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, (3): 544-553.
[10]	WANG Jiankang, ZHANG Haibo, HUANG Xianghua, DUAN Shujing. Hybrid Predictive Control for Turbo-shaft Engine Based on Online Sliding LS-SVR [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, 33(10): 1755-1764.
[11]	QIN Yuling, KONG Xianren, LUO Wenbo. Model Updating for Multi-layered Carbon Fiber Honeycomb Sandwich Panel [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2011, 32(4): 636-648.
[12]	ZHANG Xian, WANG Hongli. Failure Rate Time Series Prediction Based on Support Vector Empirical Mode Decomposition [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2011, 32(3): 480-487.
[13]	LI Jintao, FANG Jiancheng. Magnetic Shielding Method and Experiment Study of Inertial Measurement Unit Based on High Precision Fiber-optic Gyroscope [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2011, 32(11): 2106-2116.
[14]	Lu Qifeng;Zhang Weihong;Zhang Qiao;Zhu Jihong. Layout Design Optimization of Component Structure with Random Vibration Response [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2010, 31(9): 1769-1775.
[15]	Zhang Xian;Wang Hongli. Condition Time Series Prediction Using Least Squares Support Vector Machinewith Adaptive Embedding Dimension [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2010, 31(12): 2309-2314.