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

Sensors

Angshul Majumdar¹  Anupriya Gogna¹ 

[1] Indraprastha Institute of Information Technology, Delhi 110020, India; E-Mail:

关键词: EEG;    WBAN;    compressed sensing;    low-rank matrix recovery;   

DOI  :  10.3390/s140915729

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.