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Authors: C. Godin 1 ; F. Prost-Boucle 1 ; A. Campagne 2 ; S. Charbonnier 3 ; S. Bonnet 1 and A. Vidal 1

Affiliations: 1 Univ. Grenoble Alpes, CEA and LETI, France ; 2 Université Pierre Mendes, France ; 3 Univ. Grenoble Alpes & CNRS, France

Keyword(s): Emotion Recognition, Physiological Signals, Classification, Feature Selection, Heart Rate Variability, Galvanic Skin Response, Eye Blinking, Arousal, Valence, DEAP, MAHNOB-HCI.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Biosignal Acquisition, Analysis and Processing ; Data Manipulation ; Devices ; Health Engineering and Technology Applications ; Health Information Systems ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Processing of Multimodal Input ; Sensor Networks ; Soft Computing ; Wearable Sensors and Systems

Abstract: With the development of wearable physiological sensors, emotion estimation becomes a hot topic in the literature. Databases of physiological signals recorded during emotional stimulation are acquired and machine learning algorithms are used. Yet, which are the most relevant signals to detect emotions is still a question to be answered. In order to better understand the contribution of each signal, and thus sensor, to the emotion estimation problem, several feature selection algorithms were implemented on two databases freely available to the research community (DEAP and MANHOB-HCI). Both databases manipulate emotions by showing participants short videos (video clips or part of movies respectively). Features extracted from Galvanic Skin response were found to be relevant for arousal estimation in both databases. Other relevant features were eye closing rate for arousal, variance of zygomatic EMG for valence (those features being only available for DEAP). The hearth rate variability po wer in three frequency bands also appeared to be very relevant, but only for MANHOB-HCI database where heat rate was measured using ECG (whereas DEAP used PPG). This suggests that PPG is not accurate enough to estimate HRV precisely. Finally we showed on DEAP database that emotion classifiers need just a few well selected features to obtain similar performances to literature classifiers using more features. (More)


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Paper citation in several formats:
Godin, C.; Prost-Boucle, F.; Campagne, A.; Charbonnier, S.; Bonnet, S. and Vidal, A. (2015). Selection of the Most Relevant Physiological Features for Classifying Emotion. In Proceedings of the 2nd International Conference on Physiological Computing Systems - PhyCS, ISBN 978-989-758-085-7; ISSN 2184-321X, pages 17-25. DOI: 10.5220/0005238600170025

author={C. Godin. and F. Prost{-}Boucle. and A. Campagne. and S. Charbonnier. and S. Bonnet. and A. Vidal.},
title={Selection of the Most Relevant Physiological Features for Classifying Emotion},
booktitle={Proceedings of the 2nd International Conference on Physiological Computing Systems - PhyCS,},


JO - Proceedings of the 2nd International Conference on Physiological Computing Systems - PhyCS,
TI - Selection of the Most Relevant Physiological Features for Classifying Emotion
SN - 978-989-758-085-7
IS - 2184-321X
AU - Godin, C.
AU - Prost-Boucle, F.
AU - Campagne, A.
AU - Charbonnier, S.
AU - Bonnet, S.
AU - Vidal, A.
PY - 2015
SP - 17
EP - 25
DO - 10.5220/0005238600170025