Documentos de conferencia

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https://hdl.handle.net/10784/27881

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Examinando Documentos de conferencia por Autor "Castro, J."

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    An approach to emotion recognition in single-channel EEG signals using stationarywavelet transform
    (SPRINGER, 2017-01-01) Gómez, A.; Quintero, L.; López, N.; Castro, J.; Villa, L.; Mejía, G.; Gómez, A.; Quintero, L.; López, N.; Castro, J.; Villa, L.; Mejía, G.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    In this work, we perform an approach to emotion recognition from Electroencephalography (EEG) single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology. Single channel EEG signals are decomposed by several types of wavelets and each subsignal are processed using several window sizes by performing a statistical analysis. Finally, three types of classifiers were used, obtaining accuracy rate between 50% to 87% for the emotional states such as happiness, sadness and neutrality. © Springer Nature Singapore Pte Ltd. 2017.
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    An approach to emotion recognition in single-channel EEG signals: A mother child interaction
    (IOP PUBLISHING LTD, 2016-01-01) Gómez, A.; Quintero, L.; López, N.; Castro, J.; Gómez, A.; Quintero, L.; López, N.; Castro, J.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    In this work, we perform a first approach to emotion recognition from EEG single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology. Single channel EEG signals are analyzed and processed using several window sizes by performing a statistical analysis over features in the time and frequency domains. Finally, a neural network obtained an average accuracy rate of 99% of classification in two emotional states such as happiness and sadness.