Publication: Cognitive Performance Evaluation in Early Stage of Dementia: A Hybrid EEG/Eye Movement Analysis
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Issued Date
2023-01-01
Resource Type
Scopus ID
2-s2.0-85180153637
Journal Title
27th International Computer Science and Engineering Conference 2023, ICSEC 2023
Start Page
453
End Page
456
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SCOPUS
Bibliographic Citation
27th International Computer Science and Engineering Conference 2023, ICSEC 2023 (2023) , 453-456
Suggested Citation
Phothisonothai M., Pannurat N., Orkphol K., Sooknuan T., Kampeephat S., Tantisatirapong S. Cognitive Performance Evaluation in Early Stage of Dementia: A Hybrid EEG/Eye Movement Analysis. 27th International Computer Science and Engineering Conference 2023, ICSEC 2023 (2023) , 453-456. 456. doi:10.1109/ICSEC59635.2023.10329676 Retrieved from: https://hdl.handle.net/20.500.14740/20078
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Abstract
The number of people around the world with a risk of severe disease from being dementia is increasing significantly. Especially, the elderly, those with preexisting conditions are particularly at risk from the major factor to be changed to an Alzheimer's disease. The condition greatly affects the cognitive functions of the body which will eventually lead to deterioration and deformation of the body to perform regular activities. Traditional approaches have been carried out for identifying the causal factors underlying variations in terms of medical technologies, e.g., the non-invasive imaging methods and noninvasive digital biomarkers using digital signal processing. In this paper, therefore, a noninvasive hybrid approach using electroencephalography (EEG) neurofeedback and eye tracking method has been proposed to identify significant features of an early stage of dementia which later has 80% chance pro-gression of symptoms to Alzheimer's disease. The presence of modern imaging technologies which are expensive and not readily available in terms of temporal resolution response, thus, our proposed noninvasive hybrid EEG/eye tracking analysis proved to be an effective method to classify the control group from cognitively impaired group (or an early stage of dementia) using generalized linear models (GLM) classifier which obtained an average classification accuracy of 89.95%.