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Paper Abstract and Keywords
Presentation 2021-03-03 11:40
Estimation of Imagined Rhythm and Its Active Area from Electroencephalogram Using Deep Learning
Naoki Yoshimura, Toshihisa Tanaka (TUAT) EA2020-63 SIP2020-94 SP2020-28
Abstract (in Japanese) (See Japanese page) 
(in English) Rhythm is one element of music, and it is known that rhythm perception and imagery appear in an electroencephalogram (EEG). It has been reported that the imagery of equally spaced rhythms (isochronous rhythms) can be observed from EEG by frequency analysis. However, frequency analysis cannot be applied to non-isochronous rhythms with irregular intervals. This study proposes a method using a convolutional neural network to decode non-isochronous rhythm imagery from EEG. In the experiment, we recorded EEG when participants imagined a non-isochronous rhythm. Rhythm imagination performed two tasks: one in which the imagination's timing was visually indicated and the other in which no instruction was given. We trained a neural network that inputs EEG and outputs the presence or absence of imaginary sounds. As a result, even when there was no visual timing, the rhythm sound's timing could be estimated with high accuracy. From the weight of the trained neural network, it was suggested that EEG of the right temporal region is involved in the estimation of the rhythm pattern. These results suggest that any rhythm pattern can be detected from EEG and that the right temporal region's brain activity is involved in rhythm imagination.
Keyword (in Japanese) (See Japanese page) 
(in English) Rhythm Imagery / Electroencephalogram / Event Related Potential / Convolutional Neural Network / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 398, SIP2020-94, pp. 21-26, March 2021.
Paper # SIP2020-94 
Date of Issue 2021-02-24 (EA, SIP, SP) 
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 EA2020-63 SIP2020-94 SP2020-28

Conference Information
Committee EA US SP SIP IPSJ-SLP  
Conference Date 2021-03-03 - 2021-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech, Engineering/Electro Acoustics, Signal Processing, Ultrasonics, and Related Topics 
Paper Information
Registration To SIP 
Conference Code 2021-03-EA-US-SP-SIP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Estimation of Imagined Rhythm and Its Active Area from Electroencephalogram Using Deep Learning 
Sub Title (in English)  
Keyword(1) Rhythm Imagery  
Keyword(2) Electroencephalogram  
Keyword(3) Event Related Potential  
Keyword(4) Convolutional Neural Network  
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1st Author's Name Naoki Yoshimura  
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)
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Speaker Author-1 
Date Time 2021-03-03 11:40:00 
Presentation Time 25 minutes 
Registration for SIP 
Paper # EA2020-63, SIP2020-94, SP2020-28 
Volume (vol) vol.120 
Number (no) no.397(EA), no.398(SIP), no.399(SP) 
Page pp.21-26 
#Pages 6 
Date of Issue 2021-02-24 (EA, SIP, SP) 

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