Integrated neuronetwork modeling of EEG for diagnostic disorders of brain activity

Abstract

The article discusses the structured multistage modeling of EEG by means of applied mathematics to customize the input parameter space for neural network prediction. Also, the approaches and models are analyzed for their accuracy in determining the relevant signal features and adaptability to real data. The activity of potentials during brain activity is a biological process that depends on many factors and hides a space of parameters, the search for which and their definition can open us up to a new perspective on the nature and activity of the human brain. A rational way of obtaining data on brain activity is a non-invasive electroencephalogram, which registers the potential difference on the electrodes relative to the base. For further processing of the received data, it is necessary to remove noise and artifacts from them. In this work, a stan-dard algorithm of frequency filtering and filtering from noise caused by the power grid is used. After processing the data, an overview of mathematical models is offered, which with a certain degree of accuracy try to simulate the behavior of the signal or the peak moments of certain features. Added to this is the use of the LSTM model to predict the further behavior of the signal with the preliminary introduction of chaos into the model due to the modified activation function (Gaussian noise) and the input modeling of the weights.