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| Presentation | 2021-01-28 14:15 Electroencephalogram classification to motor imagery, execution, and observation of right index finger flexion using convolutional neural network Yugo Kuramura, Junya Takemoto, Tomohiko Igasaki (Kumamoto Univ.)  MICT2020-24 MBE2020-29 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We attempted to classify the EEG when the participants performed five tasks related to the right index finger flexion: kinesthetic motor imagery, visual motor imagery, no motor imagery, motor execution, and motor observation. We employed a convolutional neural network (CNN) as a classifier and compared the classification accuracy of the "batch CNN," which classifies five tasks with a single CNN, and the "segmented CNN," which combined ten CNNs that classify two tasks each. We also compared the classification accuracy when using EEGs of all nineteen sites as input data to the CNNs and using EEGs of four sites closely related to motor execution and motor observation of the index finger. As a result, for the batch CNN, we found that the classification accuracies using EEGs of nineteen and four sites were 48.2±5.9% and 46.6±6.9%, respectively. On the other hand, 52.8±9.7% and 47.5±9.4% of classification accuracies were found for the segmented CNN using EEGs of nineteen and four sites, respectively. These results suggest the effectiveness of segmented CNN using EEGs of nineteen sites as input data for classifying motor imagery, execution, and observation. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | convolutional neural network / motor imagery / motor execution / motor observation / electroencephalogram / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 349, MBE2020-29, pp. 17-21, Jan. 2021. | |
| Paper # | MBE2020-29 | |
| Date of Issue | 2021-01-21 (MICT, MBE) | |
| 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 | MICT2020-24 MBE2020-29 |
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| Conference Information | |
| Committee | MBE MICT |
| Conference Date | 2021-01-28 - 2021-01-28 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | MBE |
| Conference Code | 2021-01-MBE-MICT |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Electroencephalogram classification to motor imagery, execution, and observation of right index finger flexion using convolutional neural network |
| Sub Title (in English) | |
| Keyword(1) | convolutional neural network |
| Keyword(2) | motor imagery |
| Keyword(3) | motor execution |
| Keyword(4) | motor observation |
| Keyword(5) | electroencephalogram |
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| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Yugo Kuramura |
| 1st Author's Affiliation | Kumamoto University (Kumamoto Univ.) |
| 2nd Author's Name | Junya Takemoto |
| 2nd Author's Affiliation | Kumamoto University (Kumamoto Univ.) |
| 3rd Author's Name | Tomohiko Igasaki |
| 3rd Author's Affiliation | Kumamoto University (Kumamoto Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-01-28 14:15:00 |
| Presentation Time | 25 minutes |
| Registration for | MBE |
| Paper # | MICT2020-24, MBE2020-29 |
| Volume (vol) | vol.120 |
| Number (no) | no.348(MICT), no.349(MBE) |
| Page | pp.17-21 |
| #Pages | 5 |
| Date of Issue | 2021-01-21 (MICT, MBE) |
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