Abstract—Today it is known that the brain waves behave during relaxation through music, however, it is not yet known whether there is a pattern of dependencies between different EEG frequencies during those processes. Brain oscillations are often underestimated as compared to slower oscillations. Mean power spectra of scalp EEG signals exhibit distinct peaks emerging from the general decrease in power with increasing frequency, suggesting the existence of characteristic dependence oscillatory modes in cortical field potentials. The interactions between peaks in different frequency bands, within and between cortical EEG sources, are not well understood. The reviewed evidence supports the theory that relaxation through music can lead to behavioral and neuron chemical changes with benefic effects. This study was to address this concept by focusing on Bayesian Networks (BN) to describe the relationship between the EEG frequencies during relaxation through music. It was obtained a model with 97.7% to accuracy, in which shows the relations between each EEG signals. The dependency probability distribution was calculated, according to the signal amplitude behavior. Music changes the behavior of the low frequency signals, synchronizing them inversely proportional. Delta and theta interactions over Alpha promote increase Alpha 1 powers in relaxation through music. This event is accompanied by synchronized interaction of low-sequence signals, from Beta 1 to Gamma. Alpha 2 remains an independent variable. Further studies are needed to understand the differences between music and their subsequent effects on behavior. However, Bayesian Networks has been show to an excellent tool of EEG signal Analysis.
Key word: Bayesian Networks, Brain, Machine learning, Data mining.
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