Compressed sensing framework applying independent component analysis after undersampling for reconstructing electroencephalogram signals

Kanemoto, Daisuke; Katsumata, Shun; Aihara, Masao et al.

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2020, E103.A(12), 1647-1654

Number of Access:395（2025-08-15 20:48 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/97771

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Title
Compressed sensing framework applying independent component analysis after undersampling for reconstructing electroencephalogram signals
Creator
NRID
1000090603332 1000090603332
researchmap permalink
dkanemoto dkanemoto
Scopus Author ID
6504308983 6504308983
ORCID
0009-0002-7564-6351 0009-0002-7564-6351
CreatorName
Kanemoto, Daisuke
CreatorName
Katsumata, Shun
NRID
1000030242639 1000030242639
CreatorName
Aihara, Masao
NRID
1000050211785 1000050211785
CreatorName
Ohki, Makoto
Subject
Artifact
Compressed sensing
EEG
Independent component analysis
Random undersampling
Description
Kanemoto D., Katsumata S., Aihara M., et al. Compressed sensing framework applying independent component analysis after undersampling for reconstructing electroencephalogram signals. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E103.A, 1647 (2020); https://doi.org/10.1587/transfun.2020EAP1058.
Abstract
This paper proposes a novel compressed sensing (CS) framework for reconstructing electroencephalogram (EEG) signals. A feature of this framework is the application of independent component analysis (ICA) to remove the interference from artifacts after undersampling in a data processing unit. Therefore, we can remove the ICA processing block from the sensing unit. In this framework, we used a random undersampling measurement matrix to suppress the Gaussian. The developed framework, in which the discrete cosine transform basis and orthogonal matching pursuit were used, was evaluated using raw EEG signals with a pseudo-model of an eye-blink artifact. The normalized mean square error (NMSE) and correlation coefficient (CC), obtained as the average of 2,000 results, were compared to quantitatively demonstrate the effectiveness of the proposed framework. The evaluation results of the NMSE and CC showed that the proposed framework could remove the interference from the artifacts under a high compression ratio.
Publisher
電子情報通信学会
Institute of Electronics, Information and Communication, Engineers, IEICE
Source Title
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Volume (Issue)
E103.A(12)
Page
1647-1654
Date of Issued
2020-12-01
Language
English
Handle URL
https://hdl.handle.net/11094/97771
PISSN
09168508
eISSN
17451337
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1587/transfun.2020EAP1058
Access Rights
open access
CopyRight
Copyright © 2020 Institute of Electronics, Information and Communication, Engineers, IEICE
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article