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

Abstract

This letter reports on the effectiveness of applying the Ksingular 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.

0.36 μW/channel capacitively-coupled chopper instrumentation amplifier in EEG recording wearable devices for compressed sensing framework

“…For example, in CS, the EEG signal is assumed to be sparse when mapped on a certain basis. Various bases are known [24][25][26][27] ; for example, in the case of frequency-domain-based transforms, 28) flicker noise, which has strong frequency dependence, reportedly has significant effect on the reconstruction accuracy, while thermal noise, which has power over a wide bandwidth, has a weak effect on the transform. 29) Because the EEG signal is distributed mainly in the frequency band below 100 Hz, 30) reducing flicker noise is essential for improving the signal-tonoise ratio.…”

Compressed Sensing EEG Measurement Technique with Normally Distributed Sampling Series

“…It has been reported that approximately 70% of the total system power is consumed when the signal is transmitted by this radio [4]. Therefore, we have been studying EEG measurement frameworks using compressed sensing (CS) [5] to reduce the power consumption during wireless transmission [6]- [9].…”