Artificial intelligence for brain disease diagnosis using electroencephalogram signals

SHANG SHUNUO; SHI YINGQIAN; ZHANG YAJIE; LIU MENGXUE; ZHANG HONG; WANG PING; ZHUANG LIUJING

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Artificial intelligence for brain disease diagnosis using electroencephalogram signals

Author: SHANG SHUNUO ^{1
,

2} ; SHI YINGQIAN ; ZHANG YAJIE ; LIU MENGXUE ; ZHANG HONG ; WANG PING ; ZHUANG LIUJING
Author Information

1. Biosensor National Special Laboratory,Key Laboratory for Biomedical Engineering of Education Ministry,Department of Biomedical Engineering,Zhejiang University,Hangzhou 310027,China
2. The MOE Frontier Science Center for Brain Science&Brain-Machine Integration,Zhejiang University,Hangzhou 310027,China
Keywords: Brain disease; Electroencephalography; Brain computer interface; Artificial intelligence
From: Journal of Zhejiang University. Science. B 2024;25(10):914-940
CountryChina
Language:Chinese
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.