2021-04-122016-01-0117426596SCOPUS;2-s2.0-84978153931http://hdl.handle.net/10784/27895In 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.enghttps://v2.sherpa.ac.uk/id/publication/issn/1742-6596info:eu-repo/semantics/conferencePaperBiomedical engineeringSpeech recognitionAccuracy rateDevelopmental psychologyEmotion recognitionEmotional stateSingle channel eegSingle-channel signalsTime and frequency domainsWindow SizeBiomedical signal processing2021-04-12Gómez, A.Quintero, L.López, N.Castro, J.10.1088/1742-6596/705/1/012051