Feature extraction from EEG patterns in music listening
Various illnesses are caused by stress, and stress release is being carried out by musical therapy. A variety of music is used in the musical therapy, and it takes a long time for patient and music therapist to select the music. Generally, time selecting music can be reduced and the musical therapy can be done more easily if effective music for the purpose it is easily found. For this purpose, we measure and extract an EEG (electroencephalogram) difference between music genres as characteristic data in this paper. Our method produces data based on frequency appearance rate, extracts features by principal component analysis, and then analyzes them by using a neural network. Finally in order to show the effectiveness of the proposed method, we carried out computer simulations by using the real data.