An approach to emotion recognition in single-channel EEG signals: A mother child interaction
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.
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.
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