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Open AccessArticle

Drowsiness Estimation Using Electroencephalogram and Recurrent Support Vector Regression

1
Department of Human and Environmental Informatics, Graduate School of Science and Technology, Kumamoto University, Kumamoto 860-8555, Japan
2
Field of Biomedical and Welfare Engineering, Division of Informatics and Energy, Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto 860-8555, Japan
*
Author to whom correspondence should be addressed.
Information 2019, 10(6), 217; https://doi.org/10.3390/info10060217
Received: 24 May 2019 / Revised: 18 June 2019 / Accepted: 23 June 2019 / Published: 24 June 2019
(This article belongs to the Section Information Processes)
As a cause of accidents, drowsiness can cause economical and physical damage. A range of drowsiness estimation methods have been proposed in previous studies to aid accident prevention and address this problem. However, none of these methods are able to improve their estimation ability as the length of time or number of trials increases. Thus, in this study, we aim to find an effective drowsiness estimation method that is also able to improve its prediction ability as the subject’s activity increases. We used electroencephalogram (EEG) data to estimate drowsiness, and the Karolinska sleepiness scale (KSS) for drowsiness evaluation. Five parameters (α, β/α, (θ+α)/β, activity, and mobility) from the O1 electrode site were selected. By combining these parameters and KSS, we demonstrate that a typical support vector regression (SVR) algorithm can estimate drowsiness with a correlation coefficient (R2) of up to 0.64 and a root mean square error (RMSE) of up to 0.56. We propose a “recurrent SVR” (RSVR) method with improved estimation performance, as highlighted by an R2 value of up to 0.83, and an RMSE of up to 0.15. These results suggest that in addition to being able to estimate drowsiness based on EEG data, RSVR is able to improve its drowsiness estimation performance. View Full-Text
Keywords: drowsiness estimation; EEG; driving environment; support vector regression drowsiness estimation; EEG; driving environment; support vector regression
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Akbar, I.A.; Igasaki, T. Drowsiness Estimation Using Electroencephalogram and Recurrent Support Vector Regression. Information 2019, 10, 217.

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