Research Progress of Electroencephalogram and Eye Movement in the Diagnosis of Alzheimer's Disease

Ruihai Yan; Quanhong Che; Na Li; Feng Wang; Chongliang Zhong; Qingsong Liu

doi:10.47297/wspbdsWSP2752-630502.20260601

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Research Progress of Electroencephalogram and Eye Movement in the Diagnosis of Alzheimer's Disease

Volume 6, Issue 1

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Research Progress of Electroencephalogram and Eye Movement in the Diagnosis of Alzheimer's Disease
Research Progress of Electroencephalogram and Eye Movement in the Diagnosis of Alzheimer's Disease
ZKTECO CO., LTD., Dongguan, Guangdong, 523000, China
Vol. 6, Issue 1, Pages: 13-16(2026)
Published：30 March 2026
DOI：10.47297/wspbdsWSP2752-630502.20260601
Accepted：

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Yan Ruihai,Che Quanhong,Li Na,et al.Research Progress of Electroencephalogram and Eye Movement in the Diagnosis of Alzheimer's Disease[J].Biomedical Data Science,2026,06(01):13-16.
DOI：

Yan Ruihai,Che Quanhong,Li Na,et al.Research Progress of Electroencephalogram and Eye Movement in the Diagnosis of Alzheimer's Disease[J].Biomedical Data Science,2026,06(01):13-16. DOI： 10.47297/wspbdsWSP2752-630502.20260601.

摘要

Abstract

Early diagnosis of Alzheimer's disease (AD) is decisive for intervening in the progression of the disease and improving the quality of life of patients. Traditional clinical diagnostic methods often face limitations such as difficulty in early identification

high cost and low popularity. In recent years

electroencephalogram (EEG) and eye-tracking (ET) techniques have shown great potential in the development of digital biomarkers for AD

thanks to their non-invasive nature

high temporal resolution

low cost and ease of operation. This article provides a comprehensive review of the latest research progress of EEG and ET techniques in the field of AD diagnosis. The article first analyzed the characteristic changes in electroencephalogram (EEG) rhythms and functional network topologies in AD patients. Subsequently

abnormal behavior indicators under multiple eye movement paradigms were explored

and the integration strategies of electroencephalogram and eye movement data under feature stitching and deep learning architectures were expounded. Finally

this paper explores technological advancements such as digital cognitive twins

federated learning

and XR high ecological validity assessment in light of the latest frontier trends.

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Keywords

references

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