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Sensors 2014, 14(9), 15729-15748; doi:10.3390/s140915729

A Low-Rank Matrix Recovery Approach for Energy Efficient EEG Acquisition for a Wireless Body Area Network

1
Indraprastha Institute of Information Technology, Delhi 110020, India
2
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver V6T1Z4, Canada
*
Author to whom correspondence should be addressed.
Received: 28 March 2014 / Revised: 8 July 2014 / Accepted: 25 July 2014 / Published: 25 August 2014
(This article belongs to the Special Issue State-of-the-Art Sensors in Canada 2014)
View Full-Text   |   Download PDF [853 KB, uploaded 25 August 2014]   |  

Abstract

We address the problem of acquiring and transmitting EEG signals in Wireless Body Area Networks (WBAN) in an energy efficient fashion. In WBANs, the energy is consumed by three operations: sensing (sampling), processing and transmission. Previous studies only addressed the problem of reducing the transmission energy. For the first time, in this work, we propose a technique to reduce sensing and processing energy as well: this is achieved by randomly under-sampling the EEG signal. We depart from previous Compressed Sensing based approaches and formulate signal recovery (from under-sampled measurements) as a matrix completion problem. A new algorithm to solve the matrix completion problem is derived here. We test our proposed method and find that the reconstruction accuracy of our method is significantly better than state-of-the-art techniques; and we achieve this while saving sensing, processing and transmission energy. Simple power analysis shows that our proposed methodology consumes considerably less power compared to previous CS based techniques. View Full-Text
Keywords: EEG; WBAN; compressed sensing; low-rank matrix recovery EEG; WBAN; compressed sensing; low-rank matrix recovery
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Majumdar, A.; Gogna, A.; Ward, R. A Low-Rank Matrix Recovery Approach for Energy Efficient EEG Acquisition for a Wireless Body Area Network. Sensors 2014, 14, 15729-15748.

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