Brain melody informatics: analysing effects of music on brainwave patterns

Recently, researchers in the field of affective neuroscience have taken a keen interest in identifying patterns in brain activities that correspond to specific emotions. The relationship between music stimuli and brain waves has been of particular interest due to music's disputed effects on brain activity. While music can have an anticonvulsant effect on the brain and act as a therapeutic stimulus, it can also have proconvulsant effects such as triggering epileptic seizures. In this paper, we take a computational approach to understand the effects of different types of music on the human brain; we analyse the effects of 3 different genres of music in participants electroencephalograms (EEGs). Brain activity was recorded using a 14-channel headset from 24 participants while they listened to different music stimuli. Statistical features were extracted from the signals and useful features and channels were identified using various feature selecting techniques. Using these features we built classification models based on K-nearest Neighbour (KNN), Support Vector Machine (SVM) and Neural Network (NN). Our analysis shows that NN, along with Genetic Algorithm (GA) feature selection, can reach the highest accuracy of 97.5% in classifying the 3 music genres. The model also reaches 98.6% accuracy in classifying music based on participants' subjective rating of emotion. Additionally, the recorded brain waves identify different gamma wave levels, which are crucial in detecting epileptic seizures. Our results show that these computational techniques are effective in distinguishing music genres based on their effects on human brains.