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Paper Abstract and Keywords
Presentation 2023-03-15 13:50
Comparison of classification accuracy by frequency band restriction on emotion recognition from EEG
Raiki Yamane, Shin'ichiro Kanoh (SIT) NC2022-115
Abstract (in Japanese) (See Japanese page) 
(in English) Accuracy of emotion classification in deep learning when frequency band restriction is used as a preprocessing method for EEG was compared. In the experiment, EEG and questionnaires describing the emotional states (arousal, valence) were recorded when the subjects watched the film, and the emotional states were classified by deep learning model. As a result of this study, we achieved a 3-class classification accuracy of 85.1% for arousal and 84.31% for valence when only the γ-wave band was selected. This result shows that the classification accuracy is greatly improved by focusing on the γ-wave band for classification. Experiments on mixed emotions were also conducted to investigate methods for identifying mixed emotions.
Keyword (in Japanese) (See Japanese page) 
(in English) Emotion Recognition / EEG / Preprocessing / Deep learning / Mixed emotions / Emotion induction / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 425, NC2022-115, pp. 127-132, March 2023.
Paper # NC2022-115 
Date of Issue 2023-03-06 (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 NC2022-115

Conference Information
Committee NC MBE  
Conference Date 2023-03-13 - 2023-03-15 
Place (in Japanese) (See Japanese page) 
Place (in English) The Univ. of Electro-Communications 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Brain architecture, General 
Paper Information
Registration To NC 
Conference Code 2023-03-NC-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Comparison of classification accuracy by frequency band restriction on emotion recognition from EEG 
Sub Title (in English)  
Keyword(1) Emotion Recognition  
Keyword(2) EEG  
Keyword(3) Preprocessing  
Keyword(4) Deep learning  
Keyword(5) Mixed emotions  
Keyword(6) Emotion induction  
Keyword(7)  
Keyword(8)  
1st Author's Name Raiki Yamane  
1st Author's Affiliation Shibaura Institute of Technology (SIT)
2nd Author's Name Shin'ichiro Kanoh  
2nd Author's Affiliation Shibaura Institute of Technology (SIT)
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Speaker Author-1 
Date Time 2023-03-15 13:50:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2022-115 
Volume (vol) vol.122 
Number (no) no.425 
Page pp.127-132 
#Pages 6 
Date of Issue 2023-03-06 (NC) 

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