Row-sparse blind compressed sensing for reconstructing multi-channel EEG signals. (April 2015)

• Record Type: Journal Article
• Authors: Shukla, Ankita; Majumdar, Angshul
• Is Part Of: Biomedical signal processing and control. Volume 18(2015)
• Page Start: 174
• Page End: 178
• Publication Date: 2015-04
• Subjects: Compressed sensing -- EEG; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2014.09.003
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: This communication concentrates on application of blind compressed sensing (BCS) framework for reconstruction of multichannel electroencephalograph (EEG) signal for wireless body area networks (WBANs). Compressed sensing (CS) based techniques employ a known sparsifying basis (wavelet/DCT/Gabor). BCS learns the sparsifying dictionary while recovering the signal. The BCS framework was proposed for recovering sparse signals. A recent work showed that, EEG signals can be better recovered by exploiting inter-channel correlation. This led to a row-sparse recovery problem. In this work, we modify the basic BCS framework for recovering row-sparse signal ensembles – this leads to better EEG reconstruction accuracy compared to prior CS recovery methods. The success of this technique enables reducing the energy expenditure of the sensor nodes of the WBAN.