Music-Induced Emotion Recognition Based on Feature Reduction Using PCA From EEG Signals

Abstract

Purpose: Listening to music has a great impact on people's emotions and would change brain activity. In other words, music-induced emotions are trackable in electrical brain activities. Therefore, Electroencephalography can be a suitable tool to detect these induced emotions. The present study attempted to use electroencephalography in order to recognize four types of emotions (happy, relaxing, stressful, and sad) induced in response to listening to music excerpts, using three classifiers.

Methods: In this empirical study, electroencephalography signals were collected from 20 participants, as they were listening to pieces of selected music... The collected data was then pre-processed, and 28 linear and nonlinear features for recognizing the aforementioned emotions were extracted. Feature-space components were then reduced through a principal components analysis. Finally, the first ten components of feature-space were used as input for classifiers to identify the induced emotions.

Results: The outputs showed that the suggested method was well capable of emotion recognition.  Evaluating the music excerpts, on the self-assessment manikin scale, demonstrated that the labelling of the music tracks was accurate. The highest accuracy found among neural network, K-nearest neighbors, and support vector machine algorithms was respectively %84, %84, and %89 for happy emotions.

Conclusion: Reduction of features via principal components analysis, led to an acceptable accuracy in classification. Happiness was the most recognizable emotion and the support vector machine had the highest performance among the classifiers. In the end, the outcomes of the proposed method demonstrate that this system is better than the several research in EEG-based emotion recognition.