Analysis of folk music preference of people from different ethnic groups using kernel-based methods on EEG signals

Emotional preference of people from different ethnicity would alter multimedia implicit tagging remarkably. It can be speculated that the people from each ethnic group would prefer the folk music of their own ethnicity more than the others. An emotionally intelligent system based on electroencephalography (EEG) is proposed in this study to test this hypothesis. Four channels of EEG signals of 16 healthy subjects from different ethnic groups were recorded during 4 two-minute long excerpts of folk music. Six types of features extracted and a subset of them were selected based on minimum-Redundancy-Maximum-Relevance (mRMR) algorithm. The top-ranked features were fed to the Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel with various similarity metrics. The performance of the proposed method was assessed in terms of F1-score and accuracy (ACC) using random sub-sampling cross validation scheme. The highest performance for the single SVM classifier was achieved by Dynamic Time Warping (DTW) based RBF kernel which was significantly higher than the chance level. These results approve that the tendency of people from each ethnic group to their ethnicity is significantly reflected in their EEG signals which can be automatically detected.