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

Hamid Khabiri; Mohammad  Naseh Talebi; Mehdi Fakhimi  Kamran; Shadi  Akbari; Farzaneh  Zarrin; Fatemeh  Mohandesi

doi:10.18502/fbt.v11i1.14512

Hamid Khabiri Rasool Akram Medical Complex, Iran University of Medical Sciences, Tehran, Iran
Mohammad Naseh Talebi Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran
Mehdi Fakhimi Kamran Department of Social Cognition, Institute for Cognitive Science Studies, Tehran, Iran
Shadi Akbari Department of Psychology, University of Tabriz, Iran
Farzaneh Zarrin Department of Bioscience and Biotechnology, Malek Ashtar University of Technology, Tehran, Iran
Fatemeh Mohandesi Department of Computer and Biomedical Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran

DOI: https://doi.org/10.18502/fbt.v11i1.14512

Keywords: Emotion Recognition; Electroencephalography; Principal Component Analysis; Classification; Music.

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 to recognize four types of emotions (happy, relaxing, stressful, and sad) induced in response to listening to music excerpts, using three classifiers.

Materials and Methods: In this empirical study, electroencephalography signals were collected from 20 participants, as they were listening to pieces of selected music. The collected data were 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 three classifiers based on Neural Network (NN), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) algorithms 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 labeling of the music tracks was accurate. The highest accuracy found among NN, KNN, and SVM algorithms were %84, %84, and %89 for happy emotions, respectively.

Conclusion: The findings of this study provide useful insights into emotion classification and brain behavior related to induced emotion extraction. 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 previous research in EEG-based emotion recognition.