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Paper Abstract and Keywords
Presentation 2023-11-27 10:00
Improving the accuracy of reading visual information using resting EEG activity
Rin Nakayama, Yoshiya mori (NIT Tsuyama) NC2023-32
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we attempted to improve decoding ability in brain activity during visual stimulation by using feature values included in resting-state brain activity. Electroencephalography (EEG) recordings were conducted during both periods of resting-state and visual stimulus presentation. Subsequently, we employed an unsupervised learning approach, known as the Contrastive Variational Autoencoder (CVAE), to contrast latent variables. The results showed that the resting state data contributed to the decoding of the visual stimuli. This suggests that the resting state data contains features common to the stimulated state and can be used for decoding. Furthermore, as we manipulated the proportion of resting-state data, we observed an enhancement in the separability of stimuli with an increasing proportion of resting data. In the future, increasing the amount of data at resting-state may lead to further improvement of decoding capabilities.
Keyword (in Japanese) (See Japanese page) 
(in English) CVAE / EEG / Brain Comuputer Interface / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 285, NC2023-32, pp. 1-4, Nov. 2023.
Paper # NC2023-32 
Date of Issue 2023-11-20 (NC) 
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 NC2023-32

Conference Information
Committee NC MBE  
Conference Date 2023-11-27 - 2023-11-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Kindai Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Brain Architecture, NC, ME 
Paper Information
Registration To NC 
Conference Code 2023-11-NC-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improving the accuracy of reading visual information using resting EEG activity 
Sub Title (in English)  
Keyword(1) CVAE  
Keyword(2) EEG  
Keyword(3) Brain Comuputer Interface  
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1st Author's Name Rin Nakayama  
1st Author's Affiliation National Institute of Technology, Tsuyama College (NIT Tsuyama)
2nd Author's Name Yoshiya mori  
2nd Author's Affiliation National Institute of Technology, Tsuyama College (NIT Tsuyama)
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Speaker Author-1 
Date Time 2023-11-27 10:00:00 
Presentation Time 15 minutes 
Registration for NC 
Paper # NC2023-32 
Volume (vol) vol.123 
Number (no) no.285 
Page pp.1-4 
#Pages 4 
Date of Issue 2023-11-20 (NC) 

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