Nonlinear Symbolic Assessment of Electroencephalographic Recordings for Negative Stress Recognition

Automatic identification of negative stress is an unresolved challenge that has received great attention in the last few years. Many studies have analyzed electroencephalographic (EEG) recordings to gain new insights about how the brain reacts to both short- and long-term stressful stimuli. Although most of them have only considered linear methods, the heterogeneity and complexity of the brain has recently motivated an increasing use of nonlinear metrics. Nonetheless, brain dynamics reflected in EEG recordings often exhibit a multiscale nature and no study dealing with this aspect has been developed yet. Hence, in this work two nonlinear indices for quantifying regularity and predictability of time series from several time scales are studied for the first time to discern between visually elicited emotional states of calmness and negative stress. The obtained results have revealed the maximum discriminant ability of 86.35% for the second time scale, thus suggesting that brain dynamics triggered by negative stress can be more clearly assessed after removal of some fast temporal oscillations. Moreover, both metrics have also been able to report complementary information for some brain areas.

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Nonlinear Symbolic Assessment of Electroencephalographic Recordings for Negative Stress Recognition

Cited by 3 publications

References 30 publications

Multilag Extension of Quadratic Sample Entropy for Distress Recognition with EEG Recordings

Multilag Extension of Quadratic Sample Entropy for Distress Recognition with EEG Recordings

A Review on Nonlinear Methods Using Electroencephalographic Recordings for Emotion Recognition

Multiscale Entropy Analysis for Recognition of Visually Elicited Negative Stress From EEG Recordings

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