NDLI: Comparative analysis of physiological signals and electroencephalogram (EEG) for multimodal emotion recognition using generative models

Content Provider	IEEE Xplore Digital Library
Author	Torres-Valencia, C.A. Garcia-Arias, H.F. Alvarez Lopez, M.A. Orozco-Gutierrez, A.A.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng., Univ. Tecnol. de Pereira, Pereira, Colombia (Torres-Valencia, C.A.; Garcia-Arias, H.F.; Alvarez Lopez, M.A.; Orozco-Gutierrez, A.A.)
Abstract	Multimodal Emotion recognition (MER) is an application of machine learning were different biological signals are used in order to automatically classify a determined affective state. MER systems has been developed for different type of applications from psychological evaluation, anxiety assessment, human-machine interfaces and marketing. There are several spaces of classification proposed in the state of art for the emotion recognition task, the most known are discrete and dimensional spaces were the emotions are described in terms of some basic emotions and latent dimensions respectively. The use of dimensional spaces of classification allows a higher range of emotional states to be analyzed. The most common dimensional space used for this purpose is the Arousal/Valence space were emotions are described in terms of the intensity of the emotion that goes from inactive to active in the arousal dimension, and from unpleasant to pleasant in the valence dimension. The use of physiological signals and the EEG is well suited for emotion recognition due to the fact that an emotional states generates responses from different biological systems of the human body. Since the expression of an emotion is a dynamic process, we propose the use of generative models as Hidden Markov Models (HMM) to capture de dynamics of the signals for further classification of emotional states in terms of arousal and valence. For the development of this work an international database for emotion classification known as Dataset for Emotion Analysis using Physiological signals (DEAP) is used. The objective of this work is to determine which of the physiological and EEG signals brings more relevant information in the emotion recognition task, several experiments using HMMs from different signals and combinations of them are performed, and the results shows that some of those signals brings more discrimination between arousal and valence levels as the EEG and the Galvanic Skin Response (GSR) and the Heart rate (HR).
Starting Page	1
Ending Page	5
File Size	165427
Page Count	5
File Format	PDF
ISBN	9781479976669
DOI	10.1109/STSIVA.2014.7010181
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-17
Publisher Place	Colombia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Emotion recognition Databases Hidden Markov models Brain models Physiology Electroencephalography
Content Type	Text
Resource Type	Article

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