Document Detail

Adaptive extraction of emotion-related EEG segments using multidimensional directed information in time-frequency domain.
MedLine Citation:
PMID:  21096088     Owner:  NLM     Status:  MEDLINE    
Emotion discrimination from electroencephalogram (EEG) has gained attention the last decade as a user-friendly and effective approach to EEG-based emotion recognition (EEG-ER) systems. Nevertheless, challenging issues regarding the emotion elicitation procedure, especially its effectiveness, raise. In this work, a novel method, which not only evaluates the degree of emotion elicitation but localizes the emotion information in the time-frequency domain, as well, is proposed. The latter, incorporates multidimensional directed information at the time-frequency EEG representation, extracted using empirical mode decomposition, and introduces an asymmetry index for adaptive emotion-related EEG segment selection. Experimental results derived from 16 subjects visually stimulated with pictures from the valence/arousal space drawn from the International Affective Picture System database, justify the effectiveness of the proposed approach and its potential contribution to the enhancement of EEG-ER systems.
Panagiotis C Petrantonakis; Leontios J Hadjileontiadis
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  2010     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2010  
Date Detail:
Created Date:  2010-11-24     Completed Date:  2011-03-25     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1-4     Citation Subset:  IM    
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MeSH Terms
Electroencephalography / methods*
Emotions / physiology*
Evoked Potentials, Visual / physiology*
Signal Processing, Computer-Assisted*
Young Adult

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