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Applying K-SVD Dictionary Learning for EEG Compressed Sensing Framework with Outlier Detection and Independent Component Analysis

Kotaro NAGAI, Daisuke KANEMOTO, Makoto OHKI

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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