EEG-Based Emotion Recognition in Music Listening

• Published in: IEEE transactions on biomedical engineering Vol. 57; no. 7; pp. 1798 - 1806
• Main Authors: Lin, Yuan-Pin, Wang, Chi-Hong, Jung, Tzyy-Ping, Wu, Tien-Lin, Jeng, Shyh-Kang, Duann, Jeng-Ren, Chen, Jyh-Horng
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Affectivity. Emotion; Algorithms; Applied sciences; Artificial Intelligence; Bayes Theorem; Biological and medical sciences; Brain; Computer science; control theory; systems; EEG; Electrodes; Electroencephalography; Electroencephalography - methods; Electromyography; emotion; Emotion recognition; Emotions - physiology; Evoked Potentials, Auditory - physiology; Exact sciences and technology; Female; Fundamental and applied biological sciences. Psychology; Hospitals; Humans; IEEE activities; machine learning; Male; music; Music - psychology; Pattern Recognition, Automated - methods; Personality. Affectivity; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Signal Processing, Computer-Assisted; Support vector machine classification; Support vector machines; Human; Acquisition process; emotion; Music; EEG; Electrophysiology; Electroencephalography; machine learning; Learning algorithm; Artificial intelligence; Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2010.2048568

Ongoing brain activity can be recorded as electroen-cephalograph (EEG) to discover the links between emotional states and brain activity. This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening. A framework was proposed to optimize EEG-based emotion recognition by systematically 1) seeking emotion-specific EEG features and 2) exploring the efficacy of the classifiers. Support vector machine was employed to classify four emotional states (joy, anger, sadness, and pleasure) and obtained an averaged classification accuracy of 82.29% ± 3.06% across 26 subjects. Further, this study identified 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics during music listening. The identified features were primarily derived from electrodes placed near the frontal and the parietal lobes, consistent with many of the findings in the literature. This study might lead to a practical system for noninvasive assessment of the emotional states in practical or clinical applications.