Recognizing emotions from EEG subbands using wavelet analysis

Publication Type:
Conference Proceeding
Citation:
Proceeding in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, 2015, pp. 6030-6033., 2015, pp. 6030 - 6033 (4)
Issue Date:
2015-08-25
Full metadata record
Files in This Item:
Filename Description Size
ThumbnailEMBC15_0904_FI.pdf411.63 kB
Adobe PDF
Objectively recognizing emotions is a particularly important task to ensure that patients with emotional symptoms are given the appropriate treatments. The aim of this study was to develop an emotion recognition system using Electroencephalogram (EEG) signals to identify four emotions including happy, sad, angry, and relaxed. We approached this objective by firstly investigating the relevant EEG frequency band followed by deciding the appropriate feature extraction method. Two features were considered namely: 1. Wavelet Energy, and 2. Wavelet Entropy. EEG Channels reduction was then implemented to reduce the complexity of the features. The ground truth emotional states of each subject were inferred using Russel’s circumplex model of emotion, that is, by mapping the subjectively reported degrees of valence (pleasure) and arousal to the appropriate emotions — for example, an emotion with high valence and high arousal is equivalent to a ‘happy’ emotional state, while low valence and low arousal is equivalent to a ‘sad’ emotional state. The Support Vector Machine (SVM) classifier was then used for mapping each feature vector into corresponding discrete emotions. The results presented in this study indicated thatWavelet features extracted from alpha, beta and gamma bands seem to provide the necessary information for describing the aforementioned emotions. Using the DEAP (Dataset for Emotion Analysis using electroencephalogram, Physiological and Video Signals), our proposed method achieved an average sensitivity and specificity of 77.4% +/ 14.1% and 69.1% +/ 12.8%, respectively.
Please use this identifier to cite or link to this item: