Human emotion recognition and analysis in response to audio music using brain signals

• Published in: Computers in human behavior Vol. 65; pp. 267 - 275
• Main Authors: Bhatti, Adnan Mehmood, Majid, Muhammad, Anwar, Syed Muhammad, Khan, Bilal
• Format: Journal Article
• Language: English
• Published: Elmsford Elsevier Ltd 01.12.2016; Elsevier Science Ltd
• Subjects: Affective computing; Age; Brain; Classification; Electroencephalography; Electroencephalography (EEG); Emotion recognition; Emotions; Feature extraction; Machine learning; Men; Music; Rocks; Software; Wavelet analysis; Feature extraction; Electroencephalography (EEG); Music; Emotions; Machine learning; Classification
• ISSN: 0747-5632; 1873-7692
• DOI: 10.1016/j.chb.2016.08.029

Human emotion recognition using brain signals is an active research topic in the field of affective computing. Music is considered as a powerful tool for arousing emotions in human beings. This study recognized happy, sad, love and anger emotions in response to audio music tracks from electronic, rap, metal, rock and hiphop genres. Participants were asked to listen to audio music tracks of 1 min for each genre in a noise free environment. The main objectives of this study were to determine the effect of different genres of music on human emotions and indicating age group that is more responsive to music. Thirty men and women of three different age groups (15–25 years, 26–35 years and 36–50 years) underwent through the experiment that also included self reported emotional state after listening to each type of music. Features from three different domains i.e., time, frequency and wavelet were extracted from recorded EEG signals, which were further used by the classifier to recognize human emotions. It has been evident from results that MLP gives best accuracy to recognize human emotion in response to audio music tracks using hybrid features of brain signals. It is also observed that rock and rap genres generated happy and sad emotions respectively in subjects under study. The brain signals of age group (26–35 years) gave best emotion recognition accuracy in accordance to the self reported emotions. [Display omitted] •Brain signals are recorded using EEG headset while listening to audio music.•Four different emotions are recognized using hybrid features of EEG signals.•Emotion recognition accuracy is analyzed for three different age groups.•The relationship between music genres and human emotions is examined.