Abstract
Distress has become one of the major issues in developed countries because of its negative effects in physical and mental health. In order to control its consequences, a number of researchers have studied distress from an electroencephalographic point of view by means of the use of different nonlinear metrics. However, those studies are only based on non-lag approaches, thus many nonlinear dynamics of brain signals could not be properly assessed. In this sense, this work applies a multilag extension of a nonlinear regularity-based metric called quadratic sample entropy, in order to check the influence of the selection of a time lag for the recognition of distress with electroencephalographic recordings.
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Acknowledgements
This work was partially supported by Spanish Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación (AEI)/European Regional Development Fund (FEDER, EU) under DPI2016-80894-R and AEI TIN2015-72931-EXP grants, and by the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM) of the Instituto de Salud Carlos III. Beatriz García-Martínez holds FPU16/03740 scholarship from Spanish Ministerio de Educación, Cultura y Deporte. Arturo Martínez-Rodrigo holds EPC 2016–2017 research fund from Escuela Politécnica de Cuenca, Universidad de Castilla-La Mancha, Spain.
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García-Martínez, B., Martínez-Rodrigo, A., Fernández-Caballero, A., Alcaraz, R. (2019). Multilag Extension of Quadratic Sample Entropy for Distress Recognition with EEG Recordings. In: Novais, P., et al. Ambient Intelligence – Software and Applications –, 9th International Symposium on Ambient Intelligence. ISAmI2018 2018. Advances in Intelligent Systems and Computing, vol 806. Springer, Cham. https://doi.org/10.1007/978-3-030-01746-0_32
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