Brain oscillations are often underestimated as compared to slower oscillations. In past years, some studies had presented relationships between EEG and ECG activity. They obtained that the heart rate, and the low-frequency signals are positively correlated to the band-power changes, in the alpha and beta range. Heart rate signal showed a negative correlation with EEG signals high frequency in the time domain, however, the power band in the frequency range of beta during the action, the ECG signal is not correlated. The considerations in studying these signals aim to reduce the effects of noise, such as electromagnetic interference shielding or decreasing the signal manually, among others. This study was to address this problem by focusing on Bayesian Networks (BN) to describe the relationship between the EEG frequencies and BVP signal studied as variables. We obtained a model with 96.1% to accuracy, in which shows the relations between each EEG and ECG signal. The dependency probability distribution was calculated, according to the signal amplitude behavior. The relations for the entire signal frequency are directly proportional. When the BVP signal reaches the voltage of 34.76, induces the Delta signal to obtain a voltage of 3.36 µV and Tetha signal lowers its voltage to - 1.24 µV. An EEG signal is the result of the sum of distinct signals at different frequencies which are serially interconnected and dependent of the heart rate signals or Blood Volume Pulse.

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