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Bayesian Compressed Sensing for IoT: Application to EEG Recording

  • Itebeddine Ghorbel
  • Walma Gharbi
  • Lotfi Chaari
  • Amel Benazza
Conference paper
Part of the Advances in Predictive, Preventive and Personalised Medicine book series (APPPM, volume 10)

Abstract

Internet of Things (IoT) is a hot research topic since several years. IoT has gained a large interest in many application fields such as digital health, smart agriculture or industry. The main focus of the IoT community remains the design of appropriate applications and performant connected objects. In this paper, we address this topic from a signal processing viewpoint. We propose a model to perfom compressed sensing with connected objects where energy and communication constraints araise. The proposed model is formulated in a Bayesian framework and promising results demonstrate its potential in application to EEG signal recording from a connected MindWave device.

Keywords

IoT Compressed sensing Biomedical signal processing 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Itebeddine Ghorbel
    • 1
    • 2
  • Walma Gharbi
    • 1
    • 3
    • 4
  • Lotfi Chaari
    • 1
    • 2
  • Amel Benazza
    • 4
  1. 1.MIRACL LaboratoryUniversity of SfaxSfaxTunisia
  2. 2.Digital Research Centre of SfaxSfaxTunisia
  3. 3.Digital Research Centre of SfaxSfaxTunisia
  4. 4.SUP’COM, COSIM laboratoryUniversity of CarthageTunisTunisia

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