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Entropy 2016, 18(6), 221; doi:10.3390/e18060221

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Cuenca 16071, Spain
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Academic Editor: Kevin H. Knuth
Received: 18 April 2016 / Revised: 25 May 2016 / Accepted: 30 May 2016 / Published: 3 June 2016
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Abstract

Recognition of emotions is still an unresolved challenge, which could be helpful to improve current human-machine interfaces. Recently, nonlinear analysis of some physiological signals has shown to play a more relevant role in this context than their traditional linear exploration. Thus, the present work introduces for the first time the application of three recent entropy-based metrics: sample entropy (SE), quadratic SE (QSE) and distribution entropy (DE) to discern between emotional states of calm and negative stress (also called distress). In the last few years, distress has received growing attention because it is a common negative factor in the modern lifestyle of people from developed countries and, moreover, it may lead to serious mental and physical health problems. Precisely, 279 segments of 32-channel electroencephalographic (EEG) recordings from 32 subjects elicited to be calm or negatively stressed have been analyzed. Results provide that QSE is the first single metric presented to date with the ability to identify negative stress. Indeed, this metric has reported a discriminant ability of around 70%, which is only slightly lower than the one obtained by some previous works. Nonetheless, discriminant models from dozens or even hundreds of features have been previously obtained by using advanced classifiers to yield diagnostic accuracies about 80%. Moreover, in agreement with previous neuroanatomy findings, QSE has also revealed notable differences for all the brain regions in the neural activation triggered by the two considered emotions. Consequently, given these results, as well as easy interpretation of QSE, this work opens a new standpoint in the detection of emotional distress, which may gain new insights about the brain’s behavior under this negative emotion. View Full-Text
Keywords: EEG; distress; entropy-based measures; nonlinear analysis EEG; distress; entropy-based measures; nonlinear analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

García-Martínez, B.; Martínez-Rodrigo, A.; Zangróniz Cantabrana, R.; Pastor García, J.M.; Alcaraz, R. Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings. Entropy 2016, 18, 221.

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