Investigating the EEG effective networks of the human brain for identification of music related emotions

In this paper we investigate the brain effective networks corresponding to listening to happy and sad music. The connectivity patterns between EEG electrodes were extracted using multivariable autoregressive modeling and partial directed coherence, while participants listened to musical excerpts. Both classical and Iranian music were used as stimuli. The volunteers expressed their self-assessment using software. The results were utilized for classifying the musical excerpts. The corresponding connectivity matrices illustrated variations from happy to sad music. Also the extracted parameters from specific locations of the matrices demonstrated meaningful correlations with the subjective assessments of the emotional content of music pieces. The expressed valence was positively correlated with the input indices to the frontal channels, while this correlation was negative for sad excerpts. The proposed indices can be used for identifying the emotional perception of music pieces.