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| Presentation | 2023-01-17 14:30 Improving EOG components removal from EEG signals using independent component analysis, band power, and Pearson correlation coefficient Baijun Song, Aoi Takahi, Tomohiko Igasaki (Kumamoto Univ.)  MICT2022-51 MBE2022-51 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Eye movements are an important source of electroencephalogram (EEG) artifacts. Independent component analysis (ICA) is commonly used to detect and remove electrooculogram (EOG) components to recover EEG signals. In this study, we proposed an improved method to remove EOG components using band power and Pearson correlation coefficient (PCC). First, the band power of each independent component between 1 and 2 Hz was extracted by fast Fourier transform (FFT). As a result, a threshold value of 10⁶ μV² was set, and components with band power higher than the threshold value were considered EOG components. Then, the PCC values of each independent component were obtained by calculating the PCC values between EEG channels after removing each independent component in descending order of band power. As a result, the contribution of each independent component to each channel could be measured, and the EOG components to be removed could be estimated appropriately based on the PCC values. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | electroencephalogram / electrooculogram / independent component analysis / band power / Pearson correlation coefficient / fast Fourier transform / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 335, MBE2022-51, pp. 36-39, Jan. 2023. | |
| Paper # | MBE2022-51 | |
| Date of Issue | 2023-01-10 (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 | MICT2022-51 MBE2022-51 |
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| Conference Information | |
| Committee | MBE MICT IEE-MBE |
| Conference Date | 2023-01-17 - 2023-01-17 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | MBE |
| Conference Code | 2023-01-MBE-MICT-MBE |
| Language | English (Japanese title is available) |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Improving EOG components removal from EEG signals using independent component analysis, band power, and Pearson correlation coefficient |
| Sub Title (in English) | |
| Keyword(1) | electroencephalogram |
| Keyword(2) | electrooculogram |
| Keyword(3) | independent component analysis |
| Keyword(4) | band power |
| Keyword(5) | Pearson correlation coefficient |
| Keyword(6) | fast Fourier transform |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Baijun Song |
| 1st Author's Affiliation | Kumamoto University (Kumamoto Univ.) |
| 2nd Author's Name | Aoi Takahi |
| 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 | 2023-01-17 14:30:00 |
| Presentation Time | 25 minutes |
| Registration for | MBE |
| Paper # | MICT2022-51, MBE2022-51 |
| Volume (vol) | vol.122 |
| Number (no) | no.334(MICT), no.335(MBE) |
| Page | pp.36-39 |
| #Pages | 4 |
| Date of Issue | 2023-01-10 (MICT, MBE) |
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