Research progress of application of non-invasive brain imaging techniques in the field of pilots

Abstract

Abstract:

Research on the physiology and psychology of pilots using brain imaging techniques is of great significance in reducing flight accidents caused by human factors.Over the past decade，non-invasive brain imaging techniques are widely used due to its minimally invasive and portable nature.This paper provides a review of relevant studies from 2012 to 2024 that have used non-invasive brain imaging technology as a research tool to explore the brain structure and activity patterns of pilots.Through comparative analysis，it is found that electroencephalogram（EEG） is the preferred physiological signal for pilot brain load monitoring and selection；Magnetic resonance imaging（MRI） focuses on analyzing the changes in the brain structure and function of pilots caused by the execution of flight tasks；Near-infrared spectroscopy（NIRS） technology is mainly applied to the assessment of pilots' brain and psychological workload.Future research should explore the combined use of multiple brain function imaging techniques，which will help to further understand the physiological and psychological mechanisms of pilots.

Key words: Pilots, Brain imaging, Electroencephalogram, Magnetic resonance imaging, Near-infrared spectroscopic imaging

CLC Number:

R181.31

YE Lu, YU Xinli, YAN Dongfeng, MA Shan, CHEN Xi. Research progress of application of non-invasive brain imaging techniques in the field of pilots. [J]OCCUPATION AND HEALTH, 2026, 42(2): 279-283.

References 48

[1]	PHILIPPA G, JIM M, ADRIENNE P, et al. Monitoring the effectiveness of fatigue risk management:A survey of pilots' concerns[J]. Aerosp Med Hum Perform, 2018, 89(10):889-895. DOI URL
[2]	ORAITH H, BLANCO D E, YANG Z, et al. An evaluation of the effects of human factors on pilotage operations safety[J]. J Marine Sci Appl, 2021, 20(3):1-17. DOI
[3]	SHAPPELL S. Human error and commercial aviation accidents:An analysis using the human factors analysis and classification system[J]. Hum Factors, 2007, 49(2):227-242. DOI URL
[4]	XING L. Strategies of civil aviation pilots cultivation in the perspective of applied talent training concept[J]. CSS, 2019, 15(2):44-47.
[5]	ZHAO Y, ZHU K, ZHANG J, et al. Exploring the measurement of psychological resilience in chinese civil aviation pilots based on generalizability theory and item response theory[J]. Sci Rep, 2024, 14(1):1856. DOI PMID
[6]	魏慧琳. 多模态脑成像的有效连接分析[D]. 长沙: 国防科技大学, 2020.
[7]	张文娟, 陈泽, 张歆博, 等. 空军招飞医学选拔中脑电图检查的建议[J]. 空军航空医学, 2022, 39(6):343-345.
[8]	闫小如, 刘军. 基于脑电图机中的干扰故障检测方法研究[J]. 中国医疗设备, 2017, 32(12):52-56.
[9]	GIOVANNI C, LAURA S, YURI A, et al. Connectivity analysis in eeg data:A tutorial review of the state of the art and emerging trends[J]. Bioengineering, 2023, 10(3):372. DOI URL
[10]	崔威, 李春光, 徐嘉诚, 等. 功能性近红外光谱技术在神经疾病中的应用[J]. 中国康复理论与实践, 2020, 26(7):771-774. DOI
[11]	江茜茜, 吴宗武, 赵营, 等. 脑电图在神经重症患者中的临床应用及研究进展[J]. 现代电生理学杂志, 2017, 24(3):155-159.
[12]	袁灵, 成思航, 苏童, 等. 医学影像学的研究进展综述[J]. 中国科学:生命科学, 2021, 51(8):1130-1139.
[13]	黄继英,梁星原编著. 磁共振成像原理[M]. 西安: 陕西科学技术出版社, 1998:12-15.
[14]	高中展. 静息态功能磁共振(RS-fMRI)信号数据处理与统计分析软件开发[D]. 杭州: 杭州师范大学, 2016.
[15]	TAK S, YE J C. Statistical analysis of fNIRS data:A comprehensive review[J]. Neuro Image, 2014, 85:72-91.
[16]	HU X, LODEWIJKS G. Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures:The value of differentiation of sleepiness and mental fatigue[J]. J Safety Res, 2020, 72:173-187. DOI URL
[17]	袁翔, 孙香梅. 疲劳驾驶检测方法研究进展[J]. 汽车工程学报, 2012, 2(3):157-164.
[18]	孙崇勇. 心理负荷测量方法的现状与发展趋势[J]. 人类工效学, 2012, 18(2):88-92.
[19]	HU W D, MA J, HAN W Q. Prevention and monitoring means of flying fatigue[J]. Front Physiol, 2004, 8:542-543. DOI URL
[20]	陈勇胜, 詹皓. 双重任务操作诱发疲劳期间有关生理指标变化特征研究[J]. 空军总医院学报, 2009, 25(3):144-145.
[21]	张佳丽, 李靖, 蒙果, 等. 飞行员在模拟飞行训练中脑力疲劳的脑电图研究[J]. 中国应用生理学杂志, 2013, 29(3):267-270.
[22]	AHN S, NGUYEN T, JANG H, et al. Exploring neuro-physiological correlates of drivers mental fatigue caused by sleep deprivation using simultaneous EEG,ECG,and fNIRS Data[J]. Front Hum Neurosci, 2016, 10:219.
[23]	韩霜, 吴奇, 孙礼兵, 等. 基于深度收缩自编码网络的飞行员疲劳状态识别[J]. 生物医学工程学杂志, 2018, 35(3):443-451.
[24]	SOOYEON H, NOSANG K, TAEGEUN O, et al. Classification of pilots’ mental states using a multimodal deep learning network[J]. Biocybern Biomed Eng, 2020, 40(1):324-336. DOI URL
[25]	江文, 刘永红, 胡耿瑶, 等. 军事飞行人员脑电图检查及鉴定标准中国专家共识[J]. 解放军医学杂志, 2024, 49(2):123-130.
[26]	陈蓓蓓, 杨方, 冷秀秀, 等. 空军飞行人员脑电图检查规范[J]. 空军军医大学学报, 2022, 43(9):927-930.
[27]	于健, 刘静, 刘瑾红, 等. 1998—2007年招飞脑电图检查结果分析[J]. 海军总医院学报, 2009, 22(2):109-110.
[28]	REN A, YIN C, HAN Y, et al. Percentage amplitude changes of fMRI in patients with benign paroxysmal positional vertigo based on multi-frequency bands[J]. J Med Imaging, 2024, 34(2):6-9.
[29]	LIM D, PARK J, CHOI W H, et al. Asymptomatic brain lesions in pilots:A comparative study with non-flying personnel using brain MRI[J]. Aviat Space Environ Med, 2012, 83(9):865-71. DOI URL
[30]	孟彩丽, 刘红巾. 飞行员与普通人脑白质高信号检出率及分布比较[J]. 空军医学杂志, 2016, 32(3):161-164.
[31]	叶慧慧, 何宏建, 方静宛, 等. 大脑多模态成像技术定量研究进展[J]. 中国图象图形学报, 2022, 27(6):1944-1955.
[32]	李晓陵, 王敬贤, 李昂, 等. 基于fMRI任务态与静息态对头针疗法的研究进展[J]. 磁共振成像, 2021, 12(2):98-112.
[33]	ADAMSON M M, BAYLEY P J, SCANLON B K, et al. Pilot expertise and hippocampal size:Associations with longitudinal flight simulator performance[J]. Aviat Space Environ Med, 2012, 83(9):850-857. DOI URL
[34]	ADAMSOND M M, TAYLOR J L, HERALDEZ D, et al. Higher landing accuracy in expert pilots is associated with lower activity in the caudate nucleus[J]. PLoS One, 2014, 9(11):e112607. DOI URL
[35]	CAUSSE M, DEHAIS F, PERAN P, et al. The effects of emotion on pilot decision-making:A neuroergonomic approach to aviation safety[J]. Transp Res Part C Emerg Technol, 2013, 33:272-281. DOI URL
[36]	袁月婷, 闫光辉, 常文文, 等. 基于脑电信号空域特征的紧急制动行为识别[J]. 电子科技大学学报, 2024, 53(1):84-91.
[37]	张淑娴, 巩平, 徐芹艳, 等基于静息态功能磁共振成像的紧张性头痛度中心度研究[J]. 磁共振成像, 2024, 15(5):19-23.
[38]	LIU J, ZHANG W, QIAN L, et al. Changes in resting-state brain function of pilots after hypoxic exposure based on methods for fALFF and ReHo analysis[J]. MJCPA, 2015, 40(6):507-512.
[39]	XU K, XIAO C, CHEN X, et al. A resting-state fMRI study of pilots based on the dynamic low-frequency amplitude method[J]. Mod Comp, 2022, 28(2):44-49.
[40]	CHEN X, WANG Z, JIANG H, et al. Flight training changes the brain functional pattern in cadets[J]. Front Neurosci, 2023, 17:1120628.
[41]	XU K J, RUI L, XIPENG C, et al. Research on brain functions related to visual information processing and body coordination function of pilots based on the low-frequency amplitude method[J]. Front Hum Neurosci, 2023, 17:796526.
[42]	范金, 曾露瑶, 钟冬灵, 等. 功能性近红外光谱技术的10年发展:CiteSpace知识图谱可视化分析[J]. 中国组织工程研究, 2021, 25(23):3711-3717.
[43]	SUDA M, FUKUDA M, SATO T, et al. Subjective feeling of psychological fatigue is related to decreased reactivity in ventrolateral prefrontal cortex[J]. Brain Res, 2008, 1252:152-160. DOI URL
[44]	CAUSSE M, CHUA Z K, REMY F. Influences of age,mental workload,and flight experience on cognitive performance and prefrontal activity in private pilots:A fNIRS study[J]. Sci Rep, 2019, 9(1):7688. DOI
[45]	DEHAIS F, DUPRES A, DI F G, et al. Monitoring pilot's cognitive fatigue with engagement features in simulated and actual flight conditions using an hybrid fNIRS-EEG passive BCI[C]. IEEE International Conference on Systems, Man,and Cybernetics(SMC), 2018:544-549.
[46]	HAMANN A, CARSTENGERDES N. Investigating mental workload-induced changes in cortical oxygenation and frontal theta activity during simulated flights[J]. Sci Rep, 2022, 12(1):6449. DOI PMID
[47]	Tripp L D. On tracking the course of cerebral oxygen saturation and pilot performance during gravity-induced loss of consciousness[J]. Hum Factors, 2009, 51(6):775-784. PMID
[48]	刘煜, 潘盈朵, 李萌, 等. 近红外光谱技术在航空心理学研究中的应用与展望[J]. 心理科学, 2023, 46(6):1518-1528.