Music is an audio signal consisting of a wide variety of complex components which vary according to time and frequency. It is widely accepted in the literature that music evokes a wide variety of emotions in the audience. When a person says that the music they are listening to contains sad or happy feelings, this may not reveal the feeling they actually feel. However, according to the emotion felt during listening to music, fluctuations in electrical activity of the brain can more accurately reveal the structure of perceived true emotion. Detecting human emotions using brain signals has been the subject of current research in many areas. In this study, the problem of detection human emotions while listening to music has been discussed. Experiments are carried out both on our own dataset and on the DEAP dataset, which is widely used in the literature. Different types of Turkish music’s were played to the participants. By examining the electrical waves that occur in their brain's surface, happy, sad, relaxing and angry mood states were recognized. Participants were asked to listen to music from different types in a noiseless environment. To classification the emotions, electroencephalography (EEG) signals were saved primarily from different channels. Certain features have been extracted from these signals. Extracted features have been classified using machine learning algorithms for Support Vector Machines (SVM), K nearest neighbor (KNN), and Artificial Neural Networks (ANN). The best accuracy rate was obtained by ANN from algorithms used to train the data set and classify human emotions. According to the results obtained, it was observed that the used method performed well.