Journal
9TH INTERNATIONAL CONFERENCE ON COGNITIVE SCIENCE
Volume 97, Issue -, Pages 30-37Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.sbspro.2013.10.201
Keywords
brain signals; valence-arousal model; preschoolers; children; emotions; machine learning; classification
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In this paper, we propose a method for EEG emotion recognition which is tested based on 2 dimensional models of emotions, (1) the rSASM, and (2) the 12-PAC model. EEG data were collected from 5 preschoolers aged 5 years old while watching emotional faces from the Radboud Faces Database (RafD). Features were extracted using KSDE and MFCC and classified using MLP. Results show that EEG emotion recognition using the 12-PAC model gives the highest accuracy for both feature extraction methods. Results indicated that the accuracy of EEG emotion recognition is increased with the precision of the dimensional models. (C) 2013 The Authors. Published by Elsevier Ltd.
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