EEG Emotion Recognition Based on the Dimensional Models of Emotions

Abstract 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.

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