Semantic-based Bayesian Network to determine correlation between binaural-beats features and entrainment effects

In coping with hectic everyday lives, people have tried many different ways to reduce stress and depression. Such effort has also lead to meditation practice, which is believed and proven able to help. While meditation is beneficial, for some people, meditation is just hard to perform. These people may switch to other alternatives that give the same effects as meditation, such as through binaural beats entrainment. Many studies on brainwave entrainment have demonstrated that the brain responds by synchronizing its own electrical cycles to the same rhythm of the stimulating binaural-beats audio. The results of binaural beats entrainment towards the brainwave can be determined by monitoring the EEG readings, which can be analyzed to capture the altered brainwave patterns and qualities they exhibit. In relation to the monitoring process, our work focuses on capturing and analyzing the correlations between different binaural beats features to resulting EEG and perceived mental states. A general methodology is presented while detailing further on the proposed Semantic-based Bayesian Network Engine, which is the core mechanism employed in capturing the correlations. This novel approach is proposed firstly due to the well-known capability of Bayesian Network in modeling the elements of causal and effects. Secondly, with the introduction of semantic notion, the engine is enhanced even more for allowing dynamic-construction of Bayesian Network based on its semantics.