Evidence map›Paper›PMID 38115158›Full record

ReviewMilitary Medical Research2023

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 and 3 more

Registry-linked trialAbstract readReview
In one paragraph

Review in Military Medical Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07468162 (Analysis of the Characteristic Electroencephalogram of Patients Undergoing Orthopedic Surgery During the Induction of General Anesthesia and the Unconscious State and Recovery Period With Electroencephalogram Monitoring Device), which is not on this map. Cited by 68 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
68citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07468162 not yet recruitingnot on this mapstarted 2026, after this paper: background citation

Analysis of the Characteristic Electroencephalogram of Patients Undergoing Orthopedic Surgery During the Induction of General Anesthesia and the Unconscious State and Recovery Period With Electroencephalogram Monitoring Device

TypeobservationalSponsorFirst Affiliated Hospital of Zhejiang UniversityRan2026 to 2028Enrolled48ConditionsEEG Data Analysis, Orthopedic SurgeryArmsAfter the use of sedative drugs, the changes in the electroencephalogram (EEG) spectrum were observed with an EEG monitor
3 · Its place in the literature

Who cites it

68 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Neurofeedback for functional neurological disorders: future directions for research and clinical practice.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026
    Review
  3. Review
  4. Article
  5. Electroencephalography-Based Clustering Reveals Robust Neurophysiological Subtypes in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026
    Article
  6. Review
  7. Article
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  16. Review
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  19. Review
  20. Article

8 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Hao Zhang *School of Systems Science, Beijing Normal University, Beijing, 100875, China.
Qing-Qi Zhou *College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China.
He Chen *School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
Xiao-Qing HuDepartment of Psychology, the State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, 999077, China.
Wei-Guang LiDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China.
Yang BaiDepartment of Rehabilitation Medicine, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, China.
Jun-Xia HanBeijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China.
Yao WangSchool of Communication Science, Beijing Language and Culture University, Beijing, 100083, China.
Zhen-Hu LiangInstitute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China. zhl@ysu.edu.cn.
Dan ChenSchool of Computer Science, Wuhan University, Wuhan, 430072, China. dan.chen@whu.edu.cn.
Feng-Yu CongSchool of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116081, Liaoning, China. cong@dlut.edu.cn.
Jia-Qing YanCollege of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China. redyjq@gmail.com.ORCID 0009-0001-7469-0919
Xiao-Li LiSchool of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China. xiaolili@scut.edu.cn.

Funding

National Natural Science Foundation of China 61803003National Natural Science Foundation of China 62003228STI2030 Major Projects +2021ZD0204300
6 · The paper itself

Abstract

Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.

Indexed as

ElectroencephalographyNeurologyBrainHumansApplied principlesDiagnosisElectroencephalogram analysis methodsNeurological diseasesNeuroscience

Identifiers

PMID38115158
PMCPMC10729551

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.