Paper Abstract and Keywords |
Presentation |
2022-06-10 16:45
Motion artifact reduction in EEG recordings using the multivariate temporal response function of acceleration signals with hyperparameter estimation Hiroaki Umehara, Yusuke Yokota (NICT), Masato Okada (UTokyo/NICT), Yasuishi Naruse (NICT) NLP2022-24 CCS2022-24 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
The recent advances of wearable electroencephalography (EEG) systems with dry electrodes provide the realization of brain-computer interfaces in real-world settings. It inevitably induces artifacts by unstable contacts between the electrodes and the scalp. The reduction techniques of motion artifacts from the simultaneous measurement of the EEG and the acceleration of the electrode have been studied intensively, where the adaptive filters are implemented for the time-varying responses on the accelerations. The adaptive filters, however, involves the difficulty of the hyperparameter estimation. In this study, the forward model of the multivariate temporal function is introduced with an assumption of time invariable response. The hyperparameters are formulated based on the maximum likelihood as the Bayesian inference. With the metrics for benchmarking EEG systems,
the proposed method is more accurate than the existing ones in the ambulatory situation with varying speed of walking. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
artifact removal / movement artefact / Bayesian free energy / active electrode / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 66, CCS2022-24, pp. 123-128, June 2022. |
Paper # |
CCS2022-24 |
Date of Issue |
2022-06-02 (NLP, CCS) |
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) |
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NLP2022-24 CCS2022-24 |
Conference Information |
Committee |
CCS NLP |
Conference Date |
2022-06-09 - 2022-06-10 |
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(See Japanese page) |
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(See Japanese page) |
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Paper Information |
Registration To |
CCS |
Conference Code |
2022-06-CCS-NLP |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Motion artifact reduction in EEG recordings using the multivariate temporal response function of acceleration signals with hyperparameter estimation |
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Keyword(1) |
artifact removal |
Keyword(2) |
movement artefact |
Keyword(3) |
Bayesian free energy |
Keyword(4) |
active electrode |
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1st Author's Name |
Hiroaki Umehara |
1st Author's Affiliation |
National Institute of Information and Communications Technology (NICT) |
2nd Author's Name |
Yusuke Yokota |
2nd Author's Affiliation |
National Institute of Information and Communications Technology (NICT) |
3rd Author's Name |
Masato Okada |
3rd Author's Affiliation |
The University of Tokyo/National Institute of Information and Communications Technology (UTokyo/NICT) |
4th Author's Name |
Yasuishi Naruse |
4th Author's Affiliation |
National Institute of Information and Communications Technology (NICT) |
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Speaker |
Author-1 |
Date Time |
2022-06-10 16:45:00 |
Presentation Time |
25 minutes |
Registration for |
CCS |
Paper # |
NLP2022-24, CCS2022-24 |
Volume (vol) |
vol.122 |
Number (no) |
no.65(NLP), no.66(CCS) |
Page |
pp.123-128 |
#Pages |
6 |
Date of Issue |
2022-06-02 (NLP, CCS) |
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