The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
pandangan teks lengkap
146
Surat ini melaporkan tentang keberkesanan penggunaan pembelajaran kamus penguraian nilai tunggal (SVD) K pada rangka kerja penderiaan termampat elektroensefalogram (EEG) dengan pengesanan terpencil dan analisis komponen bebas. Menggunakan matriks kamus K-SVD dengan pengoptimuman parameter reka bentuk kami, contohnya, pada nisbah mampatan empat, kami menambah baik nilai ralat min kuasa dua ternormal sebanyak 31.4% berbanding kamus transformasi kosinus diskret untuk Pangkalan Data CHB-MIT Scalp EEG.
Kotaro NAGAI
University of Yamanashi
Daisuke KANEMOTO
Osaka University
Makoto OHKI
University of Yamanashi
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Salinan
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
Salinan
@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},}
Salinan
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 -