The applied principles of EEG analysis methods in neuroscience and clinical neurology

Hao Zhang; Qing-Qi Zhou; He Chen; Xiao-Qing Hu; Wei-Guang Li; Yang Bai; Jun-Xia Han; Yao Wang; Zhen-Hu Liang; Dan Chen; Feng-Yu Cong; Jia-Qing Yan; Xiao-Li Li

doi:10.1186/s40779-023-00502-7

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

REVIEW | Updated：2025-12-13

- The applied principles of EEG analysis methods in neuroscience and clinical neurology
- “Electroencephalography (EEG), a non-invasive measurement method for brain activity, has garnered significant interest in scientific research and medical fields due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and their applications in neuroscience and diagnosing neurological diseases. It introduces three types of EEG signals and describes five main directions for EEG analysis methods, along with different sub-methods and effect evaluations for solving the same problem. The article emphasizes the application scenarios of different EEG analysis methods and distinguishes the advantages and disadvantages of similar methods, assisting researchers in selecting suitable EEG analysis methods based on their research objectives and providing references for subsequent research.”
- Military Medical Research Vol. 11, Issue 6, Pages: 907-941(2024)
- Affiliations：
  
  1.School of Systems Science, Beijing Normal University, Beijing 100875, China
  2.College of Electrical and Control Engineering, North China University of Technology, Beijing 100041, China
  3.School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
  4.Department of Psychology, the State Key Laboratory of Brain and Cognitive Sciences, the University of Hong Kong, Hong Kong SAR 999077, China
  5.HKU-Shenzhen Institute of Research and Innovation, Shenzhen 518057, Guangdong, China
  6.Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong SAR 999077, China
  7.Department of Rehabilitation Medicine, the First Afliated Hospital of Nanchang University, Nanchang 330006, China
  8.Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang 330006, China
  9.Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing 100048, China
  10.School of Communication Science, Beijing Language and Culture University, Beijing 100083, China
  11.Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China
  12.School of Computer Science, Wuhan University, Wuhan 430072, China
  13.School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116081, Liaoning, China
  14.Guangdong Artifcial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou 510335, China
- Author bio：
  
  *zhl@ysu.edu.cn;
  dan.chen@whu.edu.cn;
  cong@dlut.edu.cn;
  redyjq@gmail.com;
  xiaolili@scut.edu.cn
- Funds：
- DOI：10.1186/s40779-023-00502-7
  CLC：
- Published：2024-12
- Accepted：
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Cite this article as: Zhang H, Zhou QQ, Chen H, Hu XQ, Li WG, Bai Y, et al. The applied principles of EEG analysis methods in neuroscience and clinical neurology. Mil Med Res. 2023;10(1):67.
DOI：

Cite this article as: Zhang H, Zhou QQ, Chen H, Hu XQ, Li WG, Bai Y, et al. The applied principles of EEG analysis methods in neuroscience and clinical neurology. Mil Med Res. 2023;10(1):67. DOI： 10.1186/s40779-023-00502-7.

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

DOI：10.1186/s40779-023-00502-7