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Paper Abstract and Keywords
Presentation 2017-03-06 17:00
Recurrent Neural Networks for task-evoked fMRI data classification
Koya Ohashi (Tokyo Tech), Taiji Suzuki (Tokyo Tech/JST/RIKEN) IBISML2016-104
Abstract (in Japanese) (See Japanese page) 
(in English) We consider a classification problem in which the task that a subject is performing is identified from the brain activity data observed by fMRI.
It has been shown that a classification method using FFNN (Feedforward Neural Network) achieved better classification accuracy than existing methods such as logistic regression.
However, their method did not use temporal information and there was room for improvement in classification accuracy.

In this study, we propose a classification method using RNN (Recurrent Neural Network) to incorporate temporal information. The proposed method adopts the idea of text classification method proposed in the field of natural language processing. Numerical experiments using real data confirmed that the proposed method achieves classification accuracy higher than those of existing methods. Furthermore, we show that through the sensitivity analysis of the learned task classifier, it is possible to estimate the part of the brain that has an important role for a specific task.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep learning / RNN / fMRI / Classification / Brain decoding / Brain machine interface / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 500, IBISML2016-104, pp. 33-40, March 2017.
Paper # IBISML2016-104 
Date of Issue 2017-02-27 (IBISML) 
ISSN Print edition: ISSN 0913-5685    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 IBISML2016-104

Conference Information
Committee IBISML  
Conference Date 2017-03-06 - 2017-03-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Tokyo Institute of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Statistical Mathematics, Machine Learning, Data Mining, etc. 
Paper Information
Registration To IBISML 
Conference Code 2017-03-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Recurrent Neural Networks for task-evoked fMRI data classification 
Sub Title (in English)  
Keyword(1) Deep learning  
Keyword(2) RNN  
Keyword(3) fMRI  
Keyword(4) Classification  
Keyword(5) Brain decoding  
Keyword(6) Brain machine interface  
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Keyword(8)  
1st Author's Name Koya Ohashi  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
2nd Author's Name Taiji Suzuki  
2nd Author's Affiliation Tokyo Institute of Technology・PRESTO,Japan Science and Technorogy Agency/RIKEN (Tokyo Tech/JST/RIKEN)
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Speaker Author-1 
Date Time 2017-03-06 17:00:00 
Presentation Time 30 minutes 
Registration for IBISML 
Paper # IBISML2016-104 
Volume (vol) vol.116 
Number (no) no.500 
Page pp.33-40 
#Pages 8 
Date of Issue 2017-02-27 (IBISML) 

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