Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 1 2023 04 22 59 68 Hamid Khabiri Department of Medical Physics and Biomedical Engineering, Tehran 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 Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran. 2022 09 07 2022 11 21 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.  https://fbt.tums.ac.ir/index.php/fbt/article/view/539 https://fbt.tums.ac.ir/index.php/fbt/article/download/539/321