Review of EEG Feature Selection by Neural Networks

Abstract

The basis of the work of electroencephalography (EEG) is the registration of electrical impulses from the brain using a special sensor or electrode. This method is used to treat and diagnose various diseases. In the past few years, due to the development of neural network technologies, the interest of researchers in EEG has noticeably increased. Neural networks for training the model require obtaining data with minimal noise distortion. In the processing of EEG signals to eliminate noise (artifacts), signal filtering and various methods for extracting signs are used. The presented manuscript provides a detailed analysis of modern methods for extracting the signs of an EEG signal used in studies of the last decade. The information presented in this paper will allow researchers to understand how to more carefully process the data of EEG signals before using neural networks to classify the signal. Due to the absence of any standards in the method of extracting EEG signs, the most important moment of this manuscript is a detailed description of the necessary steps for recognizing artifacts, which will allow researchers to maximize the potential of neural networks in the tasks of classifying EEG signal.