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On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
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Citations: Bibtex RefMan EndNote GB/T7714
Artificial intelligence for brain disease diagnosis using electroencephalogram signals
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Shunuo SHANG, Yingqian SHI, Yajie ZHANG, Mengxue LIU, Hong ZHANG, Ping WANG, Liujing ZHUANG. Artificial intelligence for brain disease diagnosis using electroencephalogram signals[J]. Journal of Zhejiang University Science B, 2024, 25(10): 914-940.
@article{title="Artificial intelligence for brain disease diagnosis using electroencephalogram signals",
author="Shunuo SHANG, Yingqian SHI, Yajie ZHANG, Mengxue LIU, Hong ZHANG, Ping WANG, Liujing ZHUANG",
journal="Journal of Zhejiang University Science B",
volume="25",
number="10",
pages="914-940",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B2400103"
}
%0 Journal Article
%T Artificial intelligence for brain disease diagnosis using electroencephalogram signals
%A Shunuo SHANG
%A Yingqian SHI
%A Yajie ZHANG
%A Mengxue LIU
%A Hong ZHANG
%A Ping WANG
%A Liujing ZHUANG
%J Journal of Zhejiang University SCIENCE B
%V 25
%N 10
%P 914-940
%@ 1673-1581
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B2400103
TY - JOUR
T1 - Artificial intelligence for brain disease diagnosis using electroencephalogram signals
A1 - Shunuo SHANG
A1 - Yingqian SHI
A1 - Yajie ZHANG
A1 - Mengxue LIU
A1 - Hong ZHANG
A1 - Ping WANG
A1 - Liujing ZHUANG
J0 - Journal of Zhejiang University Science B
VL - 25
IS - 10
SP - 914
EP - 940
%@ 1673-1581
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B2400103
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.
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