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Paper Abstract and Keywords
Presentation 2024-06-07 09:55
EEG features related to discomfort caused by braking of autonomous vehicle and their classification
Motoi Noda, Toshihisa Tanaka (TUAT), Ken Kubota, Kenichi Makita (JATCO Ltd) SIP2024-6 BioX2024-6 IE2024-6 MI2024-6
Abstract (in Japanese) (See Japanese page) 
(in English) In autonomous vehicles, drivers entrust the control of the vehicle to others (autonomous driving control devices). If the driver's expectations and the actual control differ, it may result in a significantly compromised riding experience.
Objectively evaluating and improving the discomfort felt by drivers towards autonomous driving is a crucial challenge in the practical implementation of autonomous vehicles.
Therefore, in this paper, we hypothesized that the discomfort caused by autonomous braking affects electroencephalogram (EEG).
Using a driving simulator, we simulated abnormal and normal inter-vehicle distances that cause discomfort and measured the driver's EEG.
The results of the brain wave analysis showed that the sample entropy in the frontal $theta$ band and the power in the occipital low-$beta$ band were related to the discomfort experienced during autonomous driving.
When evaluating each feature using machine learning, we correctly classified 13 out of 14 participants using the sample entropy feature in the $theta$ band.
These results suggest that the discomfort towards braking affects specific indicators in EEG.
Keyword (in Japanese) (See Japanese page) 
(in English) Automated vehicles / EEG / Frequency power / Entropy / Machine learning / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 58, SIP2024-6, pp. 29-33, June 2024.
Paper # SIP2024-6 
Date of Issue 2024-05-30 (SIP, BioX, IE, MI) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SIP2024-6 BioX2024-6 IE2024-6 MI2024-6

Conference Information
Committee ITE-ME ITE-IST BioX SIP MI IE  
Conference Date 2024-06-06 - 2024-06-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Nigata University (Ekinan-Campus "TOKIMATE") 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2024-06-ME-IST-BioX-SIP-MI-IE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) EEG features related to discomfort caused by braking of autonomous vehicle and their classification 
Sub Title (in English)  
Keyword(1) Automated vehicles  
Keyword(2) EEG  
Keyword(3) Frequency power  
Keyword(4) Entropy  
Keyword(5) Machine learning  
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1st Author's Name Motoi Noda  
1st Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
2nd Author's Name Toshihisa Tanaka  
2nd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
3rd Author's Name Ken Kubota  
3rd Author's Affiliation JATCO Ltd (JATCO Ltd)
4th Author's Name Kenichi Makita  
4th Author's Affiliation JATCO Ltd (JATCO Ltd)
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Speaker Author-1 
Date Time 2024-06-07 09:55:00 
Presentation Time 30 minutes 
Registration for SIP 
Paper # SIP2024-6, BioX2024-6, IE2024-6, MI2024-6 
Volume (vol) vol.124 
Number (no) no.58(SIP), no.59(BioX), no.60(IE), no.61(MI) 
Page pp.29-33 
#Pages 5 
Date of Issue 2024-05-30 (SIP, BioX, IE, MI) 

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