Quantitative Comparison of EEG Compressed Sensing using Gabor and K-SVD Dictionaries

Date
2018-11-20
Authors
Phuong, D
Li, XJ
Griffin, A
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE Circuits and Systems, Singapore Chapter
Abstract

With the fast development of wearable healthcare systems, compressed sensing (CS) has been proposed to be applied in electroencephalogram (EEG) acquisition. For CS, it is desired to build the best-fit dictionary in order to achieve good reconstruction accuracy. While most of existing works focused on static dictionaries such as Gabor, Fourier and wavelets, the dynamic nature of EEG signals motivates us to study learned dictionaries, which are supposed to provide better reconstruction accuracy and lower computation cost. In this paper, we provide the quantitative performance comparison of EEG CS using two different types of dictionaries, i.e., the well-known Gabor dictionaries versus K-SVD learned dictionaries. The performance comparison utilizes the well-established database of scalp EEG from Physiobank, which allows researchers in this field to compare their work with ours. In addition, it also attempts to inspire the systematic study of dictionary learning in EEG CS.

Description
Keywords
Electroencephalogram signal; Compressed sensing; Compression ratio; Gabor; K-SVD; Dictionary learning
Source
The 23rd International Conference on Digital Signal Processing (DSP 2018), 19-21 November 2018, Shanghai, China.
DOI
Rights statement
NOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in (see Citation). The original publication is available at (see Publisher's Version).