Publication: A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition
Issued Date
2021-01-01
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ISSN
15579662
00189456
00189456
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2-s2.0-85117779800
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Mahidol University
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SCOPUS
Bibliographic Citation
IEEE Transactions on Instrumentation and Measurement. Vol.70, (2021)
Suggested Citation
Supavit Kongwudhikunakorn, Suktipol Kiatthaveephong, Kamonwan Thanontip, Pitshaporn Leelaarporn, Maytus Piriyajitakonkij, Thananya Charoenpattarawut, Phairot Autthasan, Rattanaphon Chaisaen, Pathitta Dujada, Thapanun Sudhawiyangkul, Vorapun Senanarong, Theerawit Wilaiprasitporn A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition. IEEE Transactions on Instrumentation and Measurement. Vol.70, (2021). doi:10.1109/TIM.2021.3120131 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/76977
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Title
A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition
Abstract
In the status quo, dementia (DEM) is yet to be cured. Precise diagnosis prior to the onset of the symptoms can prevent the rapid progression of the emerging cognitive impairment. Recent progress has shown that electroencephalography (EEG) is the promising and cost-effective test to facilitate the detection of neurocognitive disorders. However, most of the existing works have been using only resting-state EEG. The efficiencies of EEG signals from various cognitive tasks, for DEM classification, have yet to be thoroughly investigated. In this study, we designed four cognitive tasks that engage different cognitive performances: attention, working memory, and executive function. We investigated these tasks by using statistical analysis on both time and frequency domains of EEG signals from three classes of human subjects: DEM, mild cognitive impairment (MCI), and normal control (NC). We also further evaluated the classification performances of two features extraction methods: principal component analysis (PCA) and filter bank common spatial pattern (FBCSP). We found that the working memory-related tasks yielded good performances for DEM recognition in both cases using PCA and FBCSP. Moreover, FBCSP with features combination from four tasks revealed the best sensitivity of 0.87 and the specificity of 0.80. To our best knowledge, this is the first work that concurrently investigated several cognitive tasks for DEM recognition using both statistical analysis and classification scores. Our results yielded essential information to design and aid in conducting further experimental tasks to early diagnose DEM patients.