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

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.

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

“…These proposed systems aim to explore or improve EEG-based emotion recognition systems. [2,39,41,42,49,50,57,61,63,92,104,108,109,117,131,136,149,152,157,173,174,185,186,189,191,[195][196][197][198][199][200][201][202][203][204][205][206][207][208][209]217,219,[223][224][225]229,[262][263][264][265][266]<...>…”

Symbolic Analysis of Brain Dynamics Detects Negative Stress

“…Most linear techniques proposed for the EEG processing are mainly based on the computation of its power spectral density to reach classification results between 65% and 80% [37][38][39]. More recently, the well-known sample entropy (SEn) and two modifications, i.e., quadratic SEn (QSEn) and distribution entropy (DEn), have been examined in a systematic and thorough study [40]. In this case, QSEn has shown an ability to discriminate between emotional states of calm and distress about 70%, moreover highlighting clear differences in regularity of time series obtained from different brain areas.…”

“…In this case, QSEn has shown an ability to discriminate between emotional states of calm and distress about 70%, moreover highlighting clear differences in regularity of time series obtained from different brain areas. In view of these outcomes, QSEn could be considered as one of the most promising single indices presented to date for automatic identification of distress [40]. However, this metric has only been defined to estimate regularity of time series [41], thus discarding other information contained by the data [42].…”

EEG-Based Person Authentication Using a Fuzzy Entropy-Related Approach with Two Electrodes

“…Therefore, EEG signals serve as a highly safe identifier with respect to personal authentication [6,7]. Numerous brain and psychological studies have used EEGs in order to study the neural activity underlying different emotional and psychological phenomena [8,9].…”