Music-evoked emotion classification using EEG correlation-based information

The relation between music and emotions has been investigated for decades. Most of the studies focused on short clips and were designed with specific tasks. This paper investigated the emotional states from electroencephalogram (EEG) activities during music appreciation. An emotion evoked experiment paradigm was designed during music appreciation. The EEG signals were recorded in 15 healthy adults during the entire process of music listening. The band power change (BPC) and higher order crossing (HOC) features were extracted from the EEG signals. A correlation-based feature analysis approach was proposed to find the most relevant features in time, frequency and channel space domains. From the results, this method achieved the average accuracy of 67.2% for the classification of high and low valence in the combination of BPC and HOC features. A deeper understanding of the brain emotional patterns could be helpful in building an intelligent and friendly affective application.