Music and the EEG: A Study using Nonlinear Methods

The effect of two types of music on the electroencephalogram (EEG) activity is examined, namely Indian Carnatic classical and rock music. About 300 seconds worth of EEG data is used to study the effect of each type of music. The analysis is carried out using two different methods based on nonlinear theory. The scaling properties of the EEG are studied using the detrended fluctuation analysis (DFA) algorithm, and the complexity of the electroencephalogram signal is quantified by the multiscale entropy (MSE) method. It is found that both methods show significant difference in the values of the estimated parameters for the electroencephalogram with and without music. The MSE method shows higher values of entropy for both types of music, indicating that the complexity of the electroencephalogram increases when the brain processes music.