Determination of patterns in the EEG signals during relaxation through music using Bayesian Networks
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.
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Introduction
There are a lot of experimental efforts to understand musical processing in the brain using electroencephalogram (EEG). Music induces emotion in the brain. EEG signal is applied to measure electrical activity of the brain. These EEG signal contain precious information of the different moods of subject. It is accepted that listening to music increases the theta and alpha bands power that is associated to increase relaxation.
Deore and Mehrotra1 determine the alpha rhythms in the left hemisphere are more predominant over the right hemisphere for emotions. Thus they conclude that the left region of the brain gives more response to the emotions rather than the right region. This study also shows that alpha power frequency carries useful information related to mood recognition. These features are separated using Linear Discriminate Analysis.
In other study, Deore2 show that it is possible to recognize the different moods of person using EEG signal. They observe the different brain locations as Left Hemisphere and Right Hemisphere to recognize the significance according to different moods.
Conclusion
An EEG signal is the result of the sum of distinct signals at different frequencies, which are serially interconnected and they dependent on each other signals during relaxation through music condition. Music increases the Delta, theta, and alpha bands relationship that are associated to increase relaxation; 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 Beta1 to Gamma. Alpha 2 remains an independent variable. The music changes the behavior of the low frequency signals, synchronizing them inversely proportional; when one increases the subsequent decreases. We have found that there are no studies which are designed to analyze these relationships, and we consider being of importance. These results could be used to design therapeutic methods of relaxation through music. The BN is a good tool with which we were able to study the relationships between different EEG frequencies of a signal.