Music stimuli recognition from electroencephalogram signal with machine learning

When humans are listening to music they perceive beats, rhythms and melodies. This is the basis of music stimuli recognition where the goal is to explore how music influences our brain activity. In previous studies the emotional state determination was based on users' feedback. However this method is unreliable in most cases because an emotion state is not exact and it is changing relatively slowly. In this paper we tried to recognize music-induced electroencephalogram patterns from the well-known Neurosky Mindwave Mobile device's signal with feed forward artificial neural network. The paper describes our self-developed EEG measurement framework and the efficiency of the neural network with different kinds of feature extraction strategies.