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

This letter reports on the effectiveness of applying K-singular value decomposition (SVD) dictionary learning to electroencephalogram (EEG) compressed sensing framework with outlier detection and independent component analysis. Using K-SVD matrix our design parameter optimization, for example, at compression ratio four, we improved normalized mean square error by 31.4% compared that discrete cosine transform CHB-MIT Scalp EEG Database.