Real time EEG compression for energy-aware continous mobile monitoring

Mohamed Adel Serhani, Mohamed El Menshawy, Abdelghani Benharref, Alramzana Nujum Navaz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

EEG-based mobile monitoring is recognized to be a resource-constrained activity because of the limited mobile battery, the intermittent network, and the size of EEG signal generated from continuous monitoring. In this paper, we propose a novel approach combining both EEG compression and mobile resource availability evaluation to boost and save energy for longer monitoring episode. The main core of our approach lies in developing and implementing an algorithm, which evaluates on the fly the compression cost and available resources on the mobile device to decide whether to fully/partially compress the input EEG data or not. We experimentally evaluated and tested the effectiveness of our approach using both offline and online data recorded by the Emotiv EEG device. The obtained results show that our approach significantly saves mobile battery and processing power to cope with critical health situations.

Original languageEnglish
Title of host publication2015 27th International Conference on Microelectronics, ICM 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages291-294
Number of pages4
ISBN (Electronic)9781467387590
DOIs
Publication statusPublished - Mar 21 2016
Event27th International Conference on Microelectronics, ICM 2015 - Casablanca, Morocco
Duration: Dec 20 2015Dec 23 2015

Publication series

NameProceedings of the International Conference on Microelectronics, ICM
Volume2016-March

Other

Other27th International Conference on Microelectronics, ICM 2015
Country/TerritoryMorocco
CityCasablanca
Period12/20/1512/23/15

Keywords

  • Compression
  • EEG
  • Mobile monitoring

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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