Artificial intelligence for brain disease diagnosis using electroencephalogram signals.

Shunuo Shang, Yingqian Shi, Yajie Zhang, Mengxue Liu, Hong Zhang, Ping Wang, Liujing Zhuang

Abstract readReview

In one paragraph

Review in Journal of Zhejiang University. Science. B, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it

0cells of the map it votes in

4citing papers in PubMed

–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.Neural regeneration research · 2026
Article
To the Editor- Bridging the signal gap: Body surface potential mapping and the future of early arrhythmia detection in the Global South.Heart rhythm O2 · 2026
Article
The Next Frontier in Brain Monitoring: A Comprehensive Look at In-Ear EEG Electrodes and Their Applications.Sensors (Basel, Switzerland) · 2025
Review
From prehospital care to the emergency department: current status and future directions of rapid EEG and artificial intelligence in the early recognition of status epilepticus.Frontiers in neurology
Review

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

7 authors.

Shunuo ShangBiosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.

Yingqian ShiBiosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.

Yajie ZhangBiosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.

Mengxue LiuBiosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.

Hong ZhangBiosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.

Ping WangBiosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China. zhuangliujing@zju.edu.cn.

Liujing ZhuangBiosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China. zhuangliujing@zju.edu.cn.

Funding

the National Key Research and Development Project of China 2021ZD0200405the National Natural Science Foundation of China 62271443, 32250008 and 82330064

6 · The paper itself

Abstract

Brain signals refer to electrical signals or metabolic changes that occur as a consequence of brain cell activity. Among the various non-invasive measurement methods, electroencephalogram (EEG) stands out as a widely employed technique, providing valuable insights into brain patterns. The deviations observed in EEG reading serve as indicators of abnormal brain activity, which is associated with neurological diseases. Brain‒computer interface (BCI) systems enable the direct extraction and transmission of information from the human brain, facilitating interaction with external devices. Notably, the emergence of artificial intelligence (AI) has had a profound impact on the enhancement of precision and accuracy in BCI technology, thereby broadening the scope of research in this field. AI techniques, encompassing machine learning (ML) and deep learning (DL) models, have demonstrated remarkable success in classifying and predicting various brain diseases. This comprehensive review investigates the application of AI in EEG-based brain disease diagnosis, highlighting advancements in AI algorithms.

Indexed as

Artificial IntelligenceBrain-Computer InterfacesBrain DiseasesElectroencephalographyAlgorithmsBrainDeep LearningHumansMachine LearningSignal Processing, Computer-AssistedArtificial intelligenceBrain computer interfaceBrain diseaseElectroencephalography

Identifiers

PMID39420525

DOI10.1631/jzus.B2400103

PMCPMC11494159

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.