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Evaluation and Classification of Dementia Using EEG Indicators During Brain–Computer Interface Tasks

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HCI International 2021 - Late Breaking Posters (HCII 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1499))

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Abstract

The rapid increase in the number of patients with dementia is currently a concern. According to a survey, the number of patients with dementia in Japan would exceed 10 million by 2060. Thus, there is a need to develop simple techniques for early diagnosis of dementia to suppress the increase in patients with dementia. In our laboratory, we are developing a dementia-screening tool using character input-type Brain–Computer Interface. In this study the electroencephalogram (EEG) data obtained using the tool were analyzed in the frequency band. The purpose is to find the difference in EEG between healthy people, patients with mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD). The results show that the mean value of the ratio of β to α wave (β/α) significantly differs between healthy subjects and MCI patients. The mean value of β/α was lower in the MCI patients than in the healthy subjects. In addition, there was also a significant difference in the range of β/α between β/α for patients with MCI and that for the patients with AD; that of AD patients was higher. From the results, it is considered that the degree of concentration decreases, and its variation becomes remarkable as the cognitive function declines. With these indicators, the three states are expected to be classified. In future studies, we shall verify whether the classification accuracy can be improved by using these indicators in machine learning.

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Acknowledgments

We express our sincere gratitude to the Department of Elderly Care, Tokyo Medical University, for their cooperation during this research. We also express our deep gratitude to all the collaborators that cooperated during the experiment. This research was partly supported by the fund for the development of minimally invasive treatment and diagnostic equipment, a joint research project by Tokyo Medical University and Kogakuin University. The research was also partly funded by JSPS KAKENHI, Grant Number JP19K12880.

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Authors and Affiliations

  1. Kogakuin University, 1-24-2 Nishishinjuku, Shinjuku-ku, Tokyo, 163-8677, Japan

    Yuri Nishizawa & Hisaya Tanaka

  2. Department of Geriatric Medicine, Tokyo Medical University, 6-1-1 Shinjuku, Shinjuku-ku, Tokyo, 160-8402, Japan

    Raita Fukasawa, Kentaro Hirao, Akito Tsugawa & Soichiro Shimizu

Authors
  1. Yuri Nishizawa
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  2. Hisaya Tanaka
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  3. Raita Fukasawa
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  4. Kentaro Hirao
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  5. Akito Tsugawa
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  6. Soichiro Shimizu
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Corresponding author

Correspondence to Yuri Nishizawa .

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Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  3. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

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Nishizawa, Y., Tanaka, H., Fukasawa, R., Hirao, K., Tsugawa, A., Shimizu, S. (2021). Evaluation and Classification of Dementia Using EEG Indicators During Brain–Computer Interface Tasks. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Late Breaking Posters. HCII 2021. Communications in Computer and Information Science, vol 1499. Springer, Cham. https://doi.org/10.1007/978-3-030-90179-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-90179-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90178-3

  • Online ISBN: 978-3-030-90179-0

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