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| Presentation | 2019-03-05 16:15 Performance improvement of fNIRS-BCI using generative adversarial networks Tomoyuki Nagasawa (Nagaoka Univ. of Tech.), Takanori Sato (National Inst. of Tech., Akita Col.), Isao Nambu, Yasuhiro Wada (Nagaoka Univ. of Tech.)  NC2018-72 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Since a lengthy functional near-infrared spectroscopy (fNIRS) measurement is uncomfortable for the participant, the number of data that can be acquired is limited. As a result, the training data of the classification model is insufficient; hence, the brain-computer interface (BCI) performance decreases. In this study, to improve the fNIRS-BCI accuracy, we examined an fNIRS data-augmentation method using generative adversarial networks (GANs). When applying the proposed method to the 4-class classification of a left-, right-, or both-handed ball grasping or resting, the accuracy improved after the data augmentation. This result suggests that data augmentation using GANs is useful for improving the fNIRS-BCI performance. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | generative adversarial networks / data augmentation / functional near-infrared spectroscopy / brain-computer interface / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 470, NC2018-72, pp. 151-156, March 2019. | |
| Paper # | NC2018-72 | |
| Date of Issue | 2019-02-25 (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 | NC2018-72 |
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| Conference Information | |
| Committee | NC MBE |
| Conference Date | 2019-03-04 - 2019-03-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | University of Electro Communications |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2019-03-NC-MBE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Performance improvement of fNIRS-BCI using generative adversarial networks |
| Sub Title (in English) | |
| Keyword(1) | generative adversarial networks |
| Keyword(2) | data augmentation |
| Keyword(3) | functional near-infrared spectroscopy |
| Keyword(4) | brain-computer interface |
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| 1st Author's Name | Tomoyuki Nagasawa |
| 1st Author's Affiliation | Nagaoka University of Technology (Nagaoka Univ. of Tech.) |
| 2nd Author's Name | Takanori Sato |
| 2nd Author's Affiliation | National Institute of Technology, Akita College (National Inst. of Tech., Akita Col.) |
| 3rd Author's Name | Isao Nambu |
| 3rd Author's Affiliation | Nagaoka University of Technology (Nagaoka Univ. of Tech.) |
| 4th Author's Name | Yasuhiro Wada |
| 4th Author's Affiliation | Nagaoka University of Technology (Nagaoka Univ. of Tech.) |
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| Speaker | Author-1 |
| Date Time | 2019-03-05 16:15:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2018-72 |
| Volume (vol) | vol.118 |
| Number (no) | no.470 |
| Page | pp.151-156 |
| #Pages | 6 |
| Date of Issue | 2019-02-25 (NC) |
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