Research not for publishing papers, but for fun, for satisfying curiosity, and for revealing the truth.

This blog reports latest progresses in
(1) Signal Processing and Machine Learning for Biomedicine, Neuroimaging, Wearable Healthcare, and Smart-Home
(2) Sparse Signal Recovery and Compressed Sensing of Signals by Exploiting Spatiotemporal Structures
(3) My Works


Friday, April 12, 2013

Compressed Sensing of EEG Using Wavelet Dictionary Matrices

Since my paper "Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware (IEEE T-BME, vol.60, no.1, 2013)" has been published, lots of people asked me how to do the compressed sensing of EEG using wavelets. Their problem was that Matlab has no function to generate the DWT basis matrix (i.e. the matrix D in my paper). One has to generate such matrices using other wavelet toolboxes. Now I updated my codes, where I gave a guide to generate such dictionary matrices using the wavelab (http://www-stat.stanford.edu/~wavelab/) , and there is a demo to show how to use a DWT basis matrix as the dictionary matrix for compressed sensing of EEG (demo_useDWT.m). The codes ('Compressed Sensing of EEG_v2.zip) can be downloaded at here

BTW: Please keep in mind that EEG is generally not sparse except to some special situations.