IEICE TRANSACTIONS on Fundamentals
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Applying K-SVD Dictionary Learning for EEG Compressed Sensing Framework with Outlier Detection and Independent Component Analysis
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Summary :
This letter reports on the effectiveness of applying the K-singular value decomposition (SVD) dictionary learning to the electroencephalogram (EEG) compressed sensing framework with outlier detection and independent component analysis. Using the K-SVD dictionary matrix with our design parameter optimization, for example, at compression ratio of four, we improved the normalized mean square error value by 31.4% compared with that of the discrete cosine transform dictionary for CHB-MIT Scalp EEG Database.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E104-A No.9 pp.1375-1378
- Publication Date
- 2021/09/01
- Publicized
- 2021/03/01
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2020EAL2123
- Type of Manuscript
- LETTER
- Category
- Biometrics
Authors
Kotaro NAGAI
University of Yamanashi
Daisuke KANEMOTO
Osaka University
Makoto OHKI
University of Yamanashi
Keyword
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Kotaro NAGAI, Daisuke KANEMOTO, Makoto OHKI, "Applying K-SVD Dictionary Learning for EEG Compressed Sensing Framework with Outlier Detection and Independent Component Analysis" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 9, pp. 1375-1378, September 2021, doi: 10.1587/transfun.2020EAL2123.
Abstract: This letter reports on the effectiveness of applying the K-singular value decomposition (SVD) dictionary learning to the electroencephalogram (EEG) compressed sensing framework with outlier detection and independent component analysis. Using the K-SVD dictionary matrix with our design parameter optimization, for example, at compression ratio of four, we improved the normalized mean square error value by 31.4% compared with that of the discrete cosine transform dictionary for CHB-MIT Scalp EEG Database.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020EAL2123/_p
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@ARTICLE{e104-a_9_1375,
author={Kotaro NAGAI, Daisuke KANEMOTO, Makoto OHKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Applying K-SVD Dictionary Learning for EEG Compressed Sensing Framework with Outlier Detection and Independent Component Analysis},
year={2021},
volume={E104-A},
number={9},
pages={1375-1378},
abstract={This letter reports on the effectiveness of applying the K-singular value decomposition (SVD) dictionary learning to the electroencephalogram (EEG) compressed sensing framework with outlier detection and independent component analysis. Using the K-SVD dictionary matrix with our design parameter optimization, for example, at compression ratio of four, we improved the normalized mean square error value by 31.4% compared with that of the discrete cosine transform dictionary for CHB-MIT Scalp EEG Database.},
keywords={},
doi={10.1587/transfun.2020EAL2123},
ISSN={1745-1337},
month={September},}
Copy
TY - JOUR
TI - Applying K-SVD Dictionary Learning for EEG Compressed Sensing Framework with Outlier Detection and Independent Component Analysis
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1375
EP - 1378
AU - Kotaro NAGAI
AU - Daisuke KANEMOTO
AU - Makoto OHKI
PY - 2021
DO - 10.1587/transfun.2020EAL2123
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2021
AB - This letter reports on the effectiveness of applying the K-singular value decomposition (SVD) dictionary learning to the electroencephalogram (EEG) compressed sensing framework with outlier detection and independent component analysis. Using the K-SVD dictionary matrix with our design parameter optimization, for example, at compression ratio of four, we improved the normalized mean square error value by 31.4% compared with that of the discrete cosine transform dictionary for CHB-MIT Scalp EEG Database.
ER -
English
