Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Review of Neural Networks in the EEG Signal Recognition

Version 1 : Received: 2 November 2020 / Approved: 3 November 2020 / Online: 3 November 2020 (14:07:29 CET)

How to cite: Rakhmatulin, I. Review of Neural Networks in the EEG Signal Recognition. Preprints 2020, 2020110152 (doi: 10.20944/preprints202011.0152.v1). Rakhmatulin, I. Review of Neural Networks in the EEG Signal Recognition. Preprints 2020, 2020110152 (doi: 10.20944/preprints202011.0152.v1).

Abstract

In the last decade, unprecedented progress in the development of neural networks influenced dozens of different industries, among which are signal processing for the electroencephalography process (EEG). Electroencephalography, even though it appeared in the first half of the 20th century, to this day didn’t change the physical principles of operation. But the signal processing technique due to the use of neural networks progressed significantly in this area. Evidence for this can serve that for the past 5 years more than 1000 publications on the topic of using machine learning have been published in popular libraries. Many different models of neural networks complicate the process of understanding the real situation in this area. In this manuscript, we provided the most comprehensive overview of research where were used neural networks for EEG signal processing.

Subject Areas

EEG signal recognition; machine learning in EEG; neural networks in EEG; dry electrode EEG; deep learning EEG

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