Discovering emotion-inducing music features using EEG signals

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Article

Source Title

Journal of Advanced Computational Intelligence and Intelligent Informatics

Volume

17

Issue

3

First Page

362

Last Page

370

Publication Date

1-1-2013

Abstract

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 from MIDI 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. Copyright © 2013 Fuji Technology Press Co,. Ltd.

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Digitial Object Identifier (DOI)

10.20965/jaciii.2013.p0362

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