Review of EEG feature selection by neural networks
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.
Keywords: Fast Fourier transform (FFT) EEG, wavelet transform (WT) EEG, EEG machine learning, extraction of EEG features, EEG signal filtering, EEG neural networks, bci feature selection, brain computer interface.
|Title:||Review of EEG feature selection by neural networks|
|Journal Name:||International Journal of Science and Business|
|ISSN:||ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)|
|Date of Publication:||17/08/2020|
|Paper Type:||Research Paper|
Cite This Article:
Rakhmatulin Ildar (2020). Review of EEG feature selection by neural networks. International Journal of Science and Business, 4(9), 101-112. doi: https://doi.org/10.5281/zenodo.3987894
Retrieved from https://ijsab.com/wp-content/uploads/592.pdf
About Author (s)
Rakhmatulin Ildar, PhD. South Ural State University, Department of Power Plants Networks and Systems, Chelyabinsk city, Russia, 454080.