JACIII Vol.17 No.3 pp. 362-370
doi: 10.20965/jaciii.2013.p0362


Discovering Emotion-Inducing Music Features Using EEG Signals

Rafael Cabredo*,**, Roberto Legaspi*, Paul Salvador Inventado*,**,
and Masayuki Numao*

*The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

**Center for Empathic Human-Computer Interactions, De La Salle University, 2401 Taft Avenue, Manila 1004, Philippines

October 11, 2012
January 7, 2013
May 20, 2013
music emotion recognition, machine learning, electroencephalograph
Music induces different kinds of emotions in listeners. Previous research on music and emotions discovered that different music features can be used for classifying how certain music can induce emotions in an individual. We propose a method for collecting electroencephalograph (EEG) data from subjects listening to emotion-inducing music. The EEG data is used to continuously label high-level music features with continuous-valued emotion annotations using the emotion spectrum analysis method. The music features are extracted fromMIDI files using a windowing technique. We highlight the results of two emotion models for stress and relaxation which were constructed using C4.5. Evaluations of the models using 10-fold cross validation give promising results with an average relative absolute error of 6.54% using a window length of 38.4 seconds.
Cite this article as:
R. Cabredo, R. Legaspi, P. Inventado, and M. Numao, “Discovering Emotion-Inducing Music Features Using EEG Signals,” J. Adv. Comput. Intell. Intell. Inform., Vol.17 No.3, pp. 362-370, 2013.
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