Extracting coherent emotion elicited segments from physiological signals

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

5 Citations (Scopus)

Abstract

The feasibility of real life affective detection using physiological signals is usually limited by biosensor noise and artifact. This is challenging in extracting the representative emotion features. In this paper a quasi-homogeneous segmentation algorithm based on Top-Down homogeneous splitting and Bottom-Up Merging using Bhattacharyya distance is proposed to partition the signal and remove artifacts. Furthermore, since physiological responses may also vary within one emotion elicited period, features extracted from segmented segments can better describe recent physiological patterns. In this paper a constraint-based clustering analysis based on estimating best seed of K-means is developed to discover representative emotion-elicited segments at all cross subject partitions which include labeled and unlabelled feature vectors.

Original languageEnglish
Title of host publicationIEEE SSCI 2011 - Symposium Series on Computational Intelligence - WACI 2011
Subtitle of host publication2011 Workshop on Affective Computational Intelligence
Pages55-61
Number of pages7
DOIs
Publication statusPublished - 2011
EventSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 Workshop on Affective Computational Intelligence, WACI 2011 - Paris, France
Duration: 2011 Apr 112011 Apr 15

Publication series

NameIEEE SSCI 2011 - Symposium Series on Computational Intelligence - WACI 2011: 2011 Workshop on Affective Computational Intelligence

Other

OtherSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 Workshop on Affective Computational Intelligence, WACI 2011
Country/TerritoryFrance
CityParis
Period11-04-1111-04-15

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics

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