Abstract
Early diagnosis is important in the treatment of dementia; however, many dementia patients are resist seeking medical attention. In our laboratory, we are develo** a dementia-screening device using a P300 based spelling-brain–computer interface (spelling-BCI) for early diagnosis of dementia. We believe that by estimating the results of neuropsychological examinations through measurements using the spelling-BCI, it would be possible to realize tests similar to those performed by medical specialists. According to a previous study, a multiple regression equation with Mini-Mental State Examination (MMSE), the Japanese version of the Montreal Cognitive Assessment (MoCA-J), and Frontal Assessment Battery (FAB) scores are objective variables, and the features obtained using the BCI and age are explanatory variables. In multiple regression analysis, variable selection was performed via the forward–backward stepwise selection method and the data exceeding the 95% confidence interval of the estimation error were excluded. As a result, the measurement data were removed using 95% confidence intervals for the estimation errors so that the multiple regression equation was identified using only about 30 of the more than 200 measurement data. In this study, we evaluated the estimation model of neuropsychological examinations using errors and investigated the effect of excluding outliers. As a result, in the estimation model of each neuropsychological examination, there was little change in the root mean squared errors before and after outlier removal. Therefore, the effect of outlier removal on the estimation model was insignificant.
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Yoshida, K., Tanaka, H., Fukasawa, R., Hirano, K., Tsugawa, A., Shimizu, S.: Estimating cognitive decline using P300-based spelling-brain-computer interfaces. In: Proceedings of the 7th International Symposium on Affective Science and Engineering, No. 7A-06, pp. 1–4 (2021)
Acknowledgments
We would like to thank all the collaborators who participated in our experiment. We also thank everyone at the Department of Elderly Care, Tokyo Medical University, for their cooperation. This research was supported in part by a research fund for the development of minimally invasive treatment and diagnostic equipment, a joint research project of the Tokyo Medical University and Kogakuin University. In addition, this research was supported in part by research funding from JSPS KAKENHI (Grant Number JP19K12880).
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Yoshida, K., Tanaka, H., Fukasawa, R., Hirao, K., Tsugawa, A., Shimizu, S. (2021). Multiple Regression Model for Cognitive Function Evaluation Using P300 Based Spelling-Brain–Computer Interface. 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_15
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DOI: https://doi.org/10.1007/978-3-030-90179-0_15
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