EEG-based emotion recognition in music listening.
Ongoing brain activity can be recorded as electroencephalograph (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.
Lin
Yuan-Pin
YP
Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan. yplin0115@gmail.com
Wang
Chi-Hong
CH
Jung
Tzyy-Ping
TP
Wu
Tien-Lin
TL
Jeng
Shyh-Kang
SK
Duann
Jeng-Ren
JR
Chen
Jyh-Horng
JH
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D004566:Electrodes; D004569:Electroencephalography / Q000379:methods*; D004644:Emotions / Q000502:physiology*; D005072:Evoked Potentials, Auditory / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D009146:Music / Q000523:psychology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted