An approach to emotion recognition in single-channel EEG signals using stationarywavelet transform

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.

• Fecha de publicación: 2017
• Institución: Universidad de Medellín
• Repositorio: Repositorio UDEM
• Idioma: eng
• OAI Identifier: oai:repository.udem.edu.co:11407/4354
• Acceso en línea: http://hdl.handle.net/11407/4354
• Palabra clave: EEG; Emotion; Features; KNN; QDA; RFC; Wavelet; Biomedical engineering; Electroencephalography; Electrophysiology; Signal processing; Speech recognition; Developmental psychology; Emotion; Emotion recognition; Emotional state; Features; Single channel eeg; Single-channel signals; Wavelet; Biomedical signal processing
• License: http://purl.org/coar/access_right/c_16ec