Emotion Identification From TQWT-Based EEG Rhythms

Abstract

Electroencephalogram (EEG) signals are the recording of brain electrical activity, commonly used for emotion recognition. Different EEG rhythms carry different neural dynamics. EEG rhythms are separated using tunable Q-factor wavelet transform (TQWT). Several features like mean, standard deviation, information potential are extracted from the TQWT-based EEG rhythms. Machine learning classifiers are used to differentiate various emotional states automatically. The authors have validated the proposed model using a publicly available database. Obtained classification accuracy of 92.9% proves the candidature of the proposed method for emotion identification.