EEG based patient emotion monitoring using relative wavelet energy feature and Back Propagation Neural Network

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

14 Citations (Scopus)

Abstract

In EEG-based emotion recognition, feature extraction is as important as the classification algorithm. A good choice of features results in higher recognition rate. However, there is no standard method for feature extraction in EEG-based emotion recognition, especially for real time monitoring, where speed of computation is crucial. In this work, we assess the use of relative wavelet energy as features and Back Propagation Neural Network (BPNN) as classifier for emotion recognition. This method was implemented in simulated real time emotion recognition by using a publicly accessible database. The results showed that relative wavelet energy and BPNN achieved an average recognition rate of 92.03%. The highest average recognition rate was achieved when the time window was 30s.

Original languageEnglish
Title of host publication2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2820-2823
Number of pages4
ISBN (Electronic)9781424492718
DOIs
Publication statusPublished - 4 Nov 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: 25 Aug 201529 Aug 2015

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2015-November
ISSN (Print)1557-170X

Conference

Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
CountryItaly
CityMilan
Period25/08/1529/08/15

Keywords

  • brain wave
  • EEG
  • emotion monitoring
  • emotion recognition
  • wavelet energy

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