JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE B

Accepted manuscript available online (unedited version)

Artificial intelligence for brain disease diagnosis using electroencephalogram signals

Author(s): Shunuo SHANG, Yingqian SHI, Yajie ZHANG, Mengxue LIU, Hong ZHANG, Ping WANG, Liujing ZHUANG
Affiliation(s): Biosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China; more
Corresponding email(s): zhuangliujing@zju.edu.cn, cnpwang@zju.edu.cn
Key Words: Brain disease; Electroencephalography; Brain computer interface; Artificial intelligence

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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,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.B2400103

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author="Shunuo SHANG, Yingqian SHI, Yajie ZHANG, Mengxue LIU, Hong ZHANG, Ping WANG, Liujing ZHUANG",
journal="Journal of Zhejiang University Science B",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/jzus.B2400103"
}

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%A Mengxue LIU
%A Hong ZHANG
%A Ping WANG
%A Liujing ZHUANG
%J Journal of Zhejiang University SCIENCE B
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doi="https://doi.org/10.1631/jzus.B2400103"

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A1 - Hong ZHANG
A1 - Ping WANG
A1 - Liujing ZHUANG
J0 - Journal of Zhejiang University Science B
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Y1 - in press
PB - Zhejiang University Press & Springer
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doi="https://doi.org/10.1631/jzus.B2400103"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

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.

利用脑电图信号进行脑部疾病诊断的人工智能方法

尚书诺^1,2，石颖倩¹，张娅婕¹，刘梦雪¹，张宏¹，王平^1,2,3，庄柳静^1,3
¹浙江大学生物医学工程与仪器科学学院，生物传感器国家专业实验室，生物医学工程教育部重点实验室，中国杭州市，310027
²浙江大学教育部脑与脑机融合前沿科学中心，中国杭州市，310027
³浙江大学脑机智能全国重点实验室，中国杭州市，310027
摘要：大脑信号反映了脑细胞活动引起的电信号变化或代谢变化。在各种非侵入性测量方法中，脑电图作为一种广泛应用的技术可以帮助了解大脑模式。脑电图中的异常读数可作为与神经系统疾病相关的脑活动的指标。脑机接口（BCI）系统能够直接从人脑提取和传输信息，从而实现与外部设备的交互。人工智能（AI）的出现极大地提高了BCI技术精度和准确性，并拓宽了该领域的研究范围。AI技术包括机器学习（ML）和深度学习（DL）模型，可以利用脑信号对各种脑部疾病进行分类和预测。本文综述了AI在基于脑电图的脑部疾病诊断中的应用，特别是AI算法在该领域应用的进展。

关键词组：脑部疾病；脑电图；脑机接口；人工智能

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利用脑电图信号进行脑部疾病诊断的人工智能方法

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