全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

国外脑电技术的前沿应用综述
A Review of the Frontier Application of EEG Technology Abroad

DOI: 10.12677/ACM.2023.1392049, PP. 14653-14663

Keywords: 脑电技术,脑电图,实验设计,文献计量
EEG
, Electroencephalography, Research Design, Bibliometrics

Full-Text   Cite this paper   Add to My Lib

Abstract:

脑电技术作为新兴的认知神经工具,可以将人类大脑的活动以可视化的脑电图的形式翻译出来。本文旨在总结国外各领域采用脑电技术的应用领域特点,为脑电技术未来的应用方向提供参考。本文系统梳理了2016~2021年间国外各领域中的脑电研究,分析整理了脑电技术在医学及心理学、神经工程、神经营销学等领域的应用现状。本研究发现脑电技术在医学、脑电接口及神经营销领域的应用中得到了越来越多的应用,而在信息系统领域的应用仍处于起步阶段。本文对现有的相关研究进行了系统性的梳理和分析,揭示了国外各领域采用脑电实验的特点及脑电技术的应用领域特点,拓展了学科边界,也为今后学术界的进一步研究提供了一个有用的工具。
As a new cognitive neural tool, electroencephalography (EEG) can translate human brain activities in the form of visualized EEG. This paper aims to summarize the application characteristics of EEG technology in various foreign fields and provide reference for the future application direction of EEG technology. This paper systematically reviewed the EEG research in various fields abroad from 2016 to 2021, and analyzed and summarized the application status of EEG technology in the fields of medicine, psychology, neuroengineering, neuromarketing, etc. Electroencephalography (EEG) has been applied more and more in the fields of medicine, electroencephalography interface and neu-romarketing, while its application in the field of information system is still in its infancy. This paper systematically combs and analyzes the existing relevant researches, reveals the characteristics of EEG experiments in various fields abroad and the characteristics of the application field of EEG technology, expands the discipline boundary, and provides a useful tool for further research in the academic field in the future.

References

[1]  He, Z., Yang, K., Zhuang, N. and Zeng, Y. (2021) Processing of Affective Pictures: A Study Based on Functional Con-nectivity Network in the Cerebral Cortex. Computational Intelligence and Neuroscience, 2021, Article ID: 5582666.
https://doi.org/10.1155/2021/5582666
[2]  Gkintoni, E., Meintani, P.M. and Dimakos, I. (2021) Neurocognitive and Emotional Parameters in Learning and Educational Process. 14th Annual International Conference of Education, Re-search and Innovation, Online, 8-9 November 2021, 2588-2599.
https://doi.org/10.21125/iceri.2021.0659
[3]  Bruner, E., Battaglia-Mayer, A. and Caminiti, R. (2023) The Parietal Lobe Evolution and the Emergence of Material Culture in the Human Genus. Brain Structure and Function, 228, 145-167.
https://doi.org/10.1007/s00429-022-02487-w
[4]  Palejwala, A.H., O’Connor, K.P., Pelargos, P., Briggs, R.G., Milton, C.K., Conner, A.K., Milligan, T.M., O’Donoghue, D.L., Glenn, C.A. and Sughrue, M.E. (2020) Anatomy and White Matter Connections of the Lateral Occipital Cortex. Surgical and Radiologic Anatomy, 42, 315-328.
https://doi.org/10.1007/s00276-019-02371-z
[5]  Doherty, C., Nowacki, A.S., Pat McAndrews, M., McDonald, C.R., Reyes, A., Kim, M.S., Hamberger, M., Najm, I., Bingaman, W., Jehi, L. and Busch, R.M. (2021) Predicting Mood Decline Following Temporal Lobe Epilepsy Surgery in Adults. Epilepsia, 62, 450-459.
https://doi.org/10.1111/epi.16800
[6]  Fu, Z., Wu, D.-A.J., Ross, I., Chung, J.M., Mamelak, A.N., Adolphs, R. and Rutishauser, U. (2019) Single-Neuron Correlates of Error Monitoring and Post-Error Adjustments in Human Medial Frontal Cortex. Neuron, 101, 165-177.
https://doi.org/10.1016/j.neuron.2018.11.016
[7]  庄宁. 基于脑电的情绪加工与识别技术研究[D]: [博士学位论文]. 北京: 战略支援部队信息工程大学, 2020.
[8]  Songsamoe, S., Saengwong-ngam, R., Koomhin, P. and Matan, N. (2019) Understanding Consumer Physiological and Emotional Responses to Food Products Using Electroen-cephalography (EEG). Trends in Food Science and Technology, 93, 167-173.
https://doi.org/10.1016/j.tifs.2019.09.018
[9]  Lai, C.Q., Ibrahim, H., Abdullah, M.Z., Abdullah, J.M., Suandi, S.A. and Azman, A. (2018) Literature Survey on Applications of Electroencephalography (EEG). AIP Conference Pro-ceedings, 2016, Article ID: 020070.
https://doi.org/10.1063/1.5055472
[10]  Jebelli, H., Hwang, S. and Lee, S. (2018) EEG Signal-Processing Frame-work to Obtain High-Quality Brain Waves from an Off-the-Shelf Wearable EEG Device. Journal of Computing in Civil Engineering, 32, Article ID: 04017070.
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000719
[11]  Prabhu, S., Murugan, G., Cary, M., Arulperumjothi, M. and Liu, J.-B. (2020) On Certain Distance and Degree Based Topological Indices of Zeolite LTA Frameworks. Mate-rials Research Express, 7, Article ID: 055006.
https://doi.org/10.1088/2053-1591/ab8b18
[12]  Motamedi-Fakhr, S., Moshrefi-Torbati, M., Hill, M., Hill, C.M. and White, P.R. (2014) Signal Processing Techniques Applied to Human Sleep EEG Signals—A Review. Biomedical Signal Processing and Control, 10, 21-33.
https://doi.org/10.1016/j.bspc.2013.12.003
[13]  Ramanujam, B., Dash, D. and Tripathi, M. (2018) Can Home Videos Made on Smartphones Complement Video-Eeg in Diagnosing Psychogenic Nonepileptic Seizures? Seizure, 62, 95-98.
https://doi.org/10.1016/j.seizure.2018.10.003
[14]  Davis, F.D., Riedl, R., vom Brocke, J., Léger, P.M. and Randolph, A.B. (2016) Information Systems and Neuroscience: Gmunden Retreat on NeuroIS 2016. Springer, Cham.
https://doi.org/10.1007/978-3-319-41402-7
[15]  李培楠, 包为民, 姚伟. 工程科学发展战略问题与机制完善[J]. 中国科学院院刊, 2022, 37(3): 317-325.
[16]  Lafon, B., Henin, S., Huang, Y., Friedman, D., Melloni, L., Thesen, T., Doyle, W., Buzsáki, G., Devinsky, O., Parra, L.C. and Liu, A.A. (2017) Low Frequency Transcranial Electrical Stimula-tion Does Not Entrain Sleep Rhythms Measured by Human Intracranial Recordings. Nature Communications, 8, No. 1199.
https://doi.org/10.1038/s41467-017-01045-x
[17]  Bleichner, M.G. and Debener, S. (2017) Concealed, Unobtrusive Ear-Centered EEG Acquisition: CEEGrids for Transparent EEG. Frontiers in Human Neuroscience, 11, Article 163.
https://doi.org/10.3389/fnhum.2017.00163
[18]  Kumar, J.S. and Bhuvaneswari, P. (2012) Analysis of Electroen-cephalography (EEG) Signals and Its Categorization—A Study. Procedia Engineering, 38, 2525-2536.
https://doi.org/10.1016/j.proeng.2012.06.298
[19]  Mehmood, R.M. and Lee, H.J. (2016) A Novel Feature Extrac-tion Method Based on Late Positive Potential for Emotion Recognition In Human Brain Signal Patterns. Computers and Electrical Engineering, 53, 444-457.
https://doi.org/10.1016/j.compeleceng.2016.04.009
[20]  Peterson, V., Galván, C., Hernández, H. and Spies, R. (2020) A Feasibility Study of a Complete Low-Cost Consumer-Grade Brain-Computer Interface System. Heliyon, 6, e03425.
https://doi.org/10.1016/j.heliyon.2020.e03425
[21]  Wulandari, D.P., Putri, N.G.P., Suprapto, Y.K., Pur-nami, S.W., Juniani, A.I. and Islamiyah, W.R. (2019) Epileptic Seizure Detection Based on Bandwidth Features of EEG Signals. Procedia Computer Science, 161, 568-576.
https://doi.org/10.1016/j.procs.2019.11.157
[22]  Riedl, R. and Léger, P.-M. (2016) Fundamentals of NeuroIS: In-formation Systems and the Brain. Springer, Berlin.
https://doi.org/10.1007/978-3-662-45091-8
[23]  Golnar-Nik, P., Farashi, S. and Safari, M.S. (2019) The Applica-tion of EEG Power for the Prediction and Interpretation of Consumer Decision-Making: A Neuromarketing Study. Physiology and Behavior, 207, 90-98.
https://doi.org/10.1016/j.physbeh.2019.04.025
[24]  Hsu, L. and Chen, Y.-J. (2020) Neuromarketing, Subliminal Advertising, and Hotel Selection: An EEG Study. Australasian Marketing Journal, 28, 200-208.
https://doi.org/10.1016/j.ausmj.2020.04.009
[25]  Buettner, R., Rieg, T. and Frick, J. (2020) Machine Learning Based Diagnosis of Diseases Using the Unfolded EEG Spectra: Towards an Intelligent Software Sensor. In: Davis, F., Riedl, R., vom Brocke, J., Léger, P.-M., Randolph, A. and Fischer, T., Eds., Information Systems and Neuroscience. Lecture Notes in Information Systems and Organisation, Vol. 32, Springer, Cham, 165-172.
https://doi.org/10.1007/978-3-030-28144-1_18
[26]  Marchi, N., Granata, T. and Janigro, D. (2014) Inflammatory Pathways of Seizure Disorders. Trends in Neurosciences, 37, 55-65.
https://doi.org/10.1016/j.tins.2013.11.002
[27]  Kumar, Y., Dewal, M.L. and Anand, R.S. (2013) Wavelet Entropy Based EEG Analysis for Seizure Detection. 2013 IEEE International Conference on Signal Processing, Computing and Control (ISPCC), Solan, 26-28 September 2013, 1-6.
https://doi.org/10.1109/ISPCC.2013.6663415
[28]  Cai, H., Han, J., Chen, Y., Sha, X., Wang, Z., Hu, B., Yang, J., Feng, L., Ding, Z., Chen, Y. and Gutknecht, J. (2018) A Perva-sive Approach to EEG-Based Depression Detection. Complexity, 2018, Article ID: 5238028.
https://doi.org/10.1155/2018/5238028
[29]  Peveler, R., Carson, A. and Rodin, G. (2002) Depression in Medical Patients. BMJ, 325, 149-152.
https://doi.org/10.1136/bmj.325.7356.149
[30]  Mcintyre, R.S. and O’Donovan, C. (2004) The Human Cost of Not Achieving Full Remission in Depression. Canadian Journal of Psychiatry, 49, 10S-16S.
[31]  Hasanzadeh, F., Mohebbi, M. and Rostami, R. (2019) Prediction of rTMS Treatment Response in Major Depressive Disorder Using Machine Learning Techniques and Nonlinear Features of EEG Signal. Journal of Affective Disorders, 256, 132-142.
https://doi.org/10.1016/j.jad.2019.05.070
[32]  Smitha, K.G., Vinod, A.P. and Mahesh, K. (2017) Voice Familiarity Detection Using EEG-Based Brain-Computer Interface. 2016 IEEE International Conference on Systems, Man, and Cy-bernetics (SMC), Budapest, 9-12 October 2016, 1626-1631.
https://doi.org/10.1109/SMC.2016.7844472
[33]  Tezza, D., Caprio, D., Garcia, S., Pinto, B., Laesker, D. and Andujar, M. (2020) Brain-Controlled Drone Racing Game: A Qualitative Analysis. In: Fang, X., Ed., HCI in Games. HCII 2020. Lecture Notes in Computer Science, Vol. 12211, Springer, Cham, 350-360.
https://doi.org/10.1007/978-3-030-50164-8_25
[34]  Sood, S.K. and Singh, K.D. (2018) An Optical-Fog Assisted EEG-Based Virtual Reality Framework for Enhancing E-Learning through Educational Games. Computer Applications in Engineering Education, 26, 1565-1576.
https://doi.org/10.1002/cae.21965
[35]  Galán, F., Nuttin, M., Lew, E., Ferrez, P.W., Vanacker, G., Philips, J. and del R. Millán, J. (2008) A Brain-Actuated Wheelchair: Asynchronous and Non-Invasive Brain-Computer Interfaces for Continuous Control of Robots. Clinical Neurophysiology, 119, 2159-2169.
https://doi.org/10.1016/j.clinph.2008.06.001
[36]  Liu, T., Goldberg, L., Gao, S. and Hong, B. (2010) An Online Brain-Computer Interface Using Non-Flashing Visual Evoked Potentials. Journal of Neural Engineering, 7, Article ID: 036003.
https://doi.org/10.1088/1741-2560/7/3/036003
[37]  Zhao, Q., Zhang, L. and Cichocki, A. (2009) EEG-Based Asynchronous BCI Control of a Car in 3D Virtual Reality Environments. Chinese Science Bulletin, 54, 78-87.
https://doi.org/10.1007/s11434-008-0547-3
[38]  Kasim, M.A.A., Low, C.Y., Ayub, M.A., Zakaria, N.A.C., Salleh, M.H.M., Johar, K. and Hamli, H. (2017) User-Friendly LabVIEW GUI for Prosthetic Hand Control Using Emotiv EEG Headset. Procedia Computer Science, 105, 276-281.
https://doi.org/10.1016/j.procs.2017.01.222
[39]  晁浩, 刘永利, 连卫芳. EEG情感识别中基于集成深度学习模型的多分析域特征融合[J]. 控制与决策, 2020, 35(7): 1674-1680.
[40]  Khushaba, R.N., Wise, C., Kodagoda, S., Louviere, J., Kahn, B.E. and Townsend, C. (2013) Consumer Neuroscience: Assessing the Brain Response to Marketing Stimuli Using Electroencephalogram (EEG) and Eye Tracking. Expert Systems with Applications, 40, 3803-3812.
https://doi.org/10.1016/j.eswa.2012.12.095
[41]  ?osi?, D. (2016) Neuromarketing in Market Research. Interdisciplinary Description of Complex Systems, 14, 139-147.
https://doi.org/10.7906/indecs.14.2.3
[42]  Bastiaansen, M., Straatman, S., Driessen, E., Mitas, O., Stekelenburg, J. and Wang, L. (2018) My Destination in Your Brain: A Novel Neuromarketing Approach for Evaluating the Effectiveness of Destination Marketing. Journal of Destination Marketing & Management, 7, 76-88.
https://doi.org/10.1016/j.jdmm.2016.09.003
[43]  Tromp, J., Peeters, D., Meyer, A.S. and Hagoort, P. (2018) The Combined Use of Virtual Reality and EEG to Study Language Processing in Naturalistic Environments. Behavior Re-search Methods, 50, 862-869.
https://doi.org/10.3758/s13428-017-0911-9
[44]  D’Errico, F., Leone, G., Schmid, M. and D’Anna, C. (2020) Prosocial Virtual Reality, Empathy, and EEG Measures: A Pilot Study Aimed at Monitoring Emotional Processes in In-tergroup Helping Behaviors. Applied Sciences, 10, Article No. 1196.
https://doi.org/10.3390/app10041196
[45]  Lin, F.R. and Kao, C.M. (2018) Mental Effort Detection Using EEG Data in E-Learning Contexts. Computers and Education, 122, 63-79.
https://doi.org/10.1016/j.compedu.2018.03.020
[46]  Chen, J., Li, H., Ma, L., Bo, H. and Gao, X. (2020) Application of EEMD-HHT Method on EEG Analysis for Speech Evoked Emotion Recognition. 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), Shenzhen, 6-8 August 2020, 376-381.
https://doi.org/10.1109/MIPR49039.2020.00082

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133