Paper Info
Title
Drowsiness Estimation Using Electroencephalogram And Recurrent Support Vector Regression
Abstract
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 (alpha, beta/alpha, (theta+alpha)/beta, 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 (R-2) 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 R-2 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.
Year
DOI
Venue
2019
10.3390/info10060217
INFORMATION
Keywords
Field
DocType
drowsiness estimation, EEG, driving environment, support vector regression
Data mining,Correlation coefficient,Pattern recognition,Computer science,Support vector machine,Mean squared error,Artificial intelligence,Eeg data,Accident prevention,Electroencephalography
Journal
Volume
Issue
ISSN
10
6
2078-2489
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Izzat Aulia Akbar111.09
Tomohiko Igasaki221.12