Journal
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Volume 29, Issue 2, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065718500387
Keywords
Electroencephalography; emotions recognition; distress; multiscale entropy; nonlinear metrics
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Funding
- Spanish Ministerio de Ciencia, Innovacion y Universidades
- Agencia Estatal de Investigacion (AEI)/European Regional Development Fund (FEDER, UE) [DPI2016-80894-R, TIN2016-79100-R, TIN2015-72931-EXP]
- Castilla-La Mancha Regional Government/FEDER, UE [SBPLY/17/180501/000192]
- Biomedical Research Networking Centre in Mental Health (CIBERSAM) of the Instituto de Salud Carlos III
- Spanish Ministerio de Educacion y Formacion Profesional [FPU16/03740]
- EPC 2016-2017 research fund
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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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