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| Presentation | 2022-03-04 14:20 Investigation of machine learning methods for emotion discrimination by using phase synchronization of electroencephalogram Fumiya Hirooka, Jiro Okuda (Kyoto Sangyo Univ. Grad. Sch.)  NC2021-74 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This study investigated machine learning methods for emotion discrimination by using phase synchronization of electroencephalogram (EEG). We used sparse logistic regression (SLR) and non-linear support vector machine (SVM) with spectral powers of individual EEG electrodes as well as phase locking values (PLVs) across electrodes as input features to better discriminate pleasant and unpleasant emotional states for visually presented pictures. Results showed higher discrimination accuracy when the PLVs in addition to the spectral powers were included into the input features. Particularly, the highest accuracy was achieved when discriminatory features identified by the SLR were used as input features to the SVM, under the condition that information on cross-temporal EEG changes was considered. The results of the present study suggest importance of temporal characteristics of the EEG phase synchronization in the emotion discrimination, as well as complementary use of multiple machine learning methods each having different discriminatory advantage. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Emotion Discrimination / Electroencephalogram (EEG) / Phase Locking Value (PLV) / Sparse Logistic Regression (SLR) / Support Vector Machine (SVM) / Temporal Characteristics / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 390, NC2021-74, pp. 143-148, March 2022. | |
| Paper # | NC2021-74 | |
| Date of Issue | 2022-02-23 (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 | NC2021-74 |
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| Conference Information | |
| Committee | MBE NC |
| Conference Date | 2022-03-02 - 2022-03-04 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2022-03-MBE-NC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Investigation of machine learning methods for emotion discrimination by using phase synchronization of electroencephalogram |
| Sub Title (in English) | |
| Keyword(1) | Emotion Discrimination |
| Keyword(2) | Electroencephalogram (EEG) |
| Keyword(3) | Phase Locking Value (PLV) |
| Keyword(4) | Sparse Logistic Regression (SLR) |
| Keyword(5) | Support Vector Machine (SVM) |
| Keyword(6) | Temporal Characteristics |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Fumiya Hirooka |
| 1st Author's Affiliation | Kyoto Sangyo University Graduate School (Kyoto Sangyo Univ. Grad. Sch.) |
| 2nd Author's Name | Jiro Okuda |
| 2nd Author's Affiliation | Kyoto Sangyo University Graduate School (Kyoto Sangyo Univ. Grad. Sch.) |
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| Speaker | Author-1 |
| Date Time | 2022-03-04 14:20:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2021-74 |
| Volume (vol) | vol.121 |
| Number (no) | no.390 |
| Page | pp.143-148 |
| #Pages | 6 |
| Date of Issue | 2022-02-23 (NC) |
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